From 87efc2bf4a62ad06e7622970d377d371d7eeb392 Mon Sep 17 00:00:00 2001 From: "Brooks,Steven (MED BDS) BII-CN-S" Date: Sun, 25 Feb 2024 11:44:00 +0800 Subject: [PATCH 01/49] minor formatting; adding newline breaks --- vignettes/analysis_normal.Rmd | 138 ++++++++++++++++++++++++++-------- 1 file changed, 108 insertions(+), 30 deletions(-) diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd index 07c8100..a61a937 100644 --- a/vignettes/analysis_normal.Rmd +++ b/vignettes/analysis_normal.Rmd @@ -25,17 +25,28 @@ set.seed(7015) # Background and data -In this vignette we will show the use of the Bayesian MCPMod package for continuous distributed data. -The focus lies on the utilization of an informative prior and the Bayesian MCPMod evaluation of a single trial. -We will use data that is included in the clinDR package. -More specifically, trial results of BRINTELLIX will be used to illustrate the specification of an informative prior and the usage of such a prior for the Bayesian evaluation of a (hypothetical) new trial. +In this vignette we will show the use of the Bayesian MCPMod package for +continuous distributed data. + +The focus lies on the utilization of an informative prior and the Bayesian MCPMod +evaluation of a single trial. We will use data that is included in the clinDR package. +More specifically, trial results of BRINTELLIX will be used to illustrate the +specification of an informative prior and the usage of such a prior for the +Bayesian evaluation of a (hypothetical) new trial. + BRINTELLIX is a medication used to treat major depressive disorder. -Various clinical trials with different dose groups, including control groups, were conducted. +Various clinical trials with different dose groups, including control groups, +were conducted. # Calculation of a MAP prior -In a first step, a meta analytic prior will be calculated using historical data from 4 trials with main endpoint Change from baseline in MADRS score after 8 weeks. -Please note that only information from the control group will be integrated leading to an informative mixture prior for the control group, while for the active groups a non-informative prior will be specified. +In a first step, a meta analytic prior will be calculated using historical data +from 4 trials with main endpoint Change from baseline in MADRS score after 8 weeks. + +Please note that only information from the control group will be integrated leading +to an informative mixture prior for the control group, while for the active groups +a non-informative prior will be specified. + ```{r Historical Data for Control Arm} data("metaData") dataset <- filter(as.data.frame(metaData), bname == "BRINTELLIX") @@ -53,9 +64,13 @@ hist_data <- data.frame( sd = histcontrol$sd, n = histcontrol$sampsize) ``` -Here, we suggest a function to construct a list of prior distributions for the different dose groups. + +Here, we suggest a function to construct a list of prior distributions for the +different dose groups. + This function is adapted to the needs of this example. Other applications may need a different way to construct prior distributions. + ```{r Defining MAP prior function} getPriorList <- function ( @@ -109,7 +124,9 @@ getPriorList <- function ( } ``` + With the dose levels to be investigated, the prior distribution can be constructed. + ```{r Getting the MAP prior} dose_levels <- c(0, 2.5, 5, 10) @@ -123,9 +140,14 @@ getESS(prior_list) # Specifications for the new trial -To be able to apply the Bayesian MCPMod approach, candidate models need to be specified using functions from the R package DoseFinding. -Since there are only 3 active dose levels we will limit the set of candidate models to a linear, an exponential, and an emax model. +To be able to apply the Bayesian MCPMod approach, candidate models need to be +specified using functions from the R package DoseFinding. + +Since there are only 3 active dose levels we will limit the set of candidate +models to a linear, an exponential, and an emax model. + Note that the linear candidate model does not require a guesstimate. + ```{r Pre-Specification of candidate models} exp_guesst <- DoseFinding::guesst( d = 5, @@ -146,7 +168,9 @@ mods <- DoseFinding::Mods( maxEff = -1, placEff = -12.8) ``` + We will use the trial with ct.gov number NCT00735709 as exemplary new trial. + ```{r new trial} new_trial <- filter( dataset, @@ -156,8 +180,14 @@ new_trial <- filter( n_patients <- c(128, 124, 129, 122) ``` + # Combination of prior information and trial results -As outlined in Fleischer et al. [Bayesian MCPMod. Pharmaceutical Statistics. 2022; 21(3): 654-670.], in a first step the posterior is calculated combining the prior information with the estimated results of the new trial. Via the summary function it is possible to print out the summary information of the posterior distributions. + +As outlined in Fleischer et al. [Bayesian MCPMod. Pharmaceutical Statistics. 2022; 21(3): 654-670.], +in a first step the posterior is calculated combining the prior information with +the estimated results of the new trial. Via the summary function it is possible +to print out the summary information of the posterior distributions. + ```{r Trial results} posterior <- getPosterior( prior = prior_list, @@ -167,12 +197,20 @@ posterior <- getPosterior( summary(posterior) ``` + # Execution of Bayesian MCPMod Test step -For the execution of the testing step of Bayesian MCPMod a critical value on the probability scale will be determined for a given alpha level. -This critical value is calculated using the re-estimated contrasts for the frequentist MCPMod to ensure that, when using non-informative priors, the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled by the specified alpha level. +For the execution of the testing step of Bayesian MCPMod a critical value on the +probability scale will be determined for a given alpha level. + +This critical value is calculated using the re-estimated contrasts for the +frequentist MCPMod to ensure that, when using non-informative priors, +the actual error level for falsely declaring a significant trial in the Bayesian +MCPMod is controlled by the specified alpha level. + +A pseudo-optimal contrast matrix is generated based on the variability of the +posterior distribution (see Fleischer et al. 2022 for more details). -A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see Fleischer et al. 2022 for more details). ```{r Preparation of input for Bayesian MCPMod Test step} crit_pval <- getCritProb( mods = mods, @@ -185,8 +223,11 @@ contr_mat <- getContr( dose_levels = dose_levels, sd_posterior = summary(posterior)[, 2]) ``` + Please note that there are different ways to derive the contrasts. -The following code shows the implementation of some of these ways but it is not executed and the contrast specification above is used. +The following code shows the implementation of some of these ways but it is not +executed and the contrast specification above is used. + ```{r , eval = FALSE} # i) the frequentist contrast contr_mat_prior <- getContr( @@ -206,7 +247,10 @@ contr_mat_prior <- getContr( dose_weights = n_patients, prior_list = prior_list) ``` -The Bayesian MCP testing step is then executed based on the posterior information, the provided contrasts and the multiplicity adjusted critical value. + +The Bayesian MCP testing step is then executed based on the posterior information, +the provided contrasts and the multiplicity adjusted critical value. + ```{r Execution of Bayesian MCPMod Test step} BMCP_result <- performBayesianMCP( posterior_list = posterior, @@ -215,19 +259,27 @@ BMCP_result <- performBayesianMCP( BMCP_result ``` + The testing step is significant indicating a non-flat dose-response shape. -In detail, all 3 models are significant and the p-value for the emax model indicates deviation from the null hypothesis the most. +In detail, all 3 models are significant and the p-value for the emax model +indicates deviation from the null hypothesis the most. # Model fitting and visualization of results In the model fitting step the posterior distribution is used as basis. Both simplified and full fitting are performed. -For the simplified fit, the multivariate normal distribution of the control group is approximated and reduced by a one-dimensional normal distribution. -The actual fit (on this approximated posterior distribution) is then performed using generalized least squares criterion. -In contrast, for the full fit, the non-linear optimization problem is addressed via the Nelder Mead algorithm. +For the simplified fit, the multivariate normal distribution of the control group +is approximated and reduced by a one-dimensional normal distribution. + +The actual fit (on this approximated posterior distribution) is then performed +using generalized least squares criterion. In contrast, for the full fit, the +non-linear optimization problem is addressed via the Nelder Mead algorithm. + +The output of the fit includes information about the predicted effects for the +included dose levels, the generalized AIC, and the corresponding weights. -The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. For the considered case, the simplified and the full fit are very similar. + ```{r Model fitting} # Option a) Simplified approach by using approximated posterior distribution fit_simple <- getModelFits( @@ -243,24 +295,41 @@ fit <- getModelFits( posterior = posterior, simple = FALSE) ``` -Via the predict() function, one can also receive estimates for dose levels that were not included in the trial. + +Via the predict() function, one can also receive estimates for dose levels that +were not included in the trial. + ```{r Predict} predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) ``` -It is possible to plot the fitted dose response models and an AIC based average model (black lines). + +It is possible to plot the fitted dose response models and an AIC based average +model (black lines). + ```{r Plot simple vs fit} plot(fit_simple) plot(fit) ``` -To assess the uncertainty, one can additionally visualize credible bands (orange shaded areas, default levels are 50% and 95%). + +To assess the uncertainty, one can additionally visualize credible bands +(orange shaded areas, default levels are 50% and 95%). + These credible bands are calculated with a bootstrap method as follows: -Samples from the posterior distribution are drawn and for every sample the simplified fitting step and a prediction is performed. -These predictions are then used to identify and visualize the specified quantiles. + +- Samples from the posterior distribution are drawn and for every sample the +simplified fitting step and a prediction is performed. + +- These predictions are then used to identify and visualize the specified quantiles. + ```{r Plot with bootstrap} plot(fit, cr_bands = TRUE) ``` -The bootstrap based quantiles can also be directly calculated via the getBootstrapQuantiles() function. + +The bootstrap based quantiles can also be directly calculated via the +getBootstrapQuantiles() function. + For this example, only 6 quantiles are bootstrapped for each model fit. + ```{r Bootstrap} getBootstrapQuantiles( model_fits = fit, @@ -268,12 +337,21 @@ getBootstrapQuantiles( doses = c(0, 2.5, 4, 5, 7, 10), n_samples = 6) ``` -Technical note: The median quantile of the bootstrap based procedure is not necessary similar to the main model fit, as they are derived via different procedures. -The main fit, i.e. the black lines in the plot, show the best fit of a certain model based on minimizing the residuals for the posterior distribution, while the bootstrap based 50% quantile shows the median fit of the random sampling and fitting procedure. + +Technical note: The median quantile of the bootstrap based procedure is not +necessary similar to the main model fit, as they are derived via different procedures. + +The main fit, i.e. the black lines in the plot, show the best fit of a certain model +based on minimizing the residuals for the posterior distribution, while the bootstrap +based 50% quantile shows the median fit of the random sampling and fitting procedure. # Additional note -It is also possible to perform the testing and modeling step in a combined fashion via the performBayesianMCPMod() function. + +It is also possible to perform the testing and modeling step in a combined fashion +via the performBayesianMCPMod() function. + This code serves merely as an example and is not run in this vignette. + ```{r, eval = FALSE} performBayesianMCPMod( posterior_list = posterior, From cae733aedc0a34c4dcd6452d8ecaafc86f7e9112 Mon Sep 17 00:00:00 2001 From: "Brooks,Steven (MED BDS) BII-CN-S" Date: Sun, 25 Feb 2024 12:08:04 +0800 Subject: [PATCH 02/49] added references.bib; adjusted .qmd for refs links --- ...nalysis_normal.Rmd => analysis_normal.qmd} | 37 ++++++++++++------- vignettes/references.bib | 25 +++++++++++++ 2 files changed, 49 insertions(+), 13 deletions(-) rename vignettes/{analysis_normal.Rmd => analysis_normal.qmd} (92%) create mode 100644 vignettes/references.bib diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.qmd similarity index 92% rename from vignettes/analysis_normal.Rmd rename to vignettes/analysis_normal.qmd index a61a937..a08320c 100644 --- a/vignettes/analysis_normal.Rmd +++ b/vignettes/analysis_normal.qmd @@ -1,10 +1,18 @@ --- title: "Analysis Example of Bayesian MCPMod for Continuous Data" -output: rmarkdown::html_vignette -number_sections: true +subtitle: "WORK IN PROGRESS" +date: today +format: + html: + fig-height: 3.5 + code-fold: show + self-contained: true + toc: true + number-sections: true + bibliography: references.bib vignette: > %\VignetteIndexEntry{Analysis Example of Bayesian MCPMod for Continuous Data} - %\VignetteEngine{knitr::rmarkdown} + %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} --- @@ -169,7 +177,8 @@ mods <- DoseFinding::Mods( placEff = -12.8) ``` -We will use the trial with ct.gov number NCT00735709 as exemplary new trial. +We will use the trial with ct.gov number NCT00735709 as exemplary new trial +[@nct00735709_2024a]. ```{r new trial} new_trial <- filter( @@ -183,10 +192,11 @@ n_patients <- c(128, 124, 129, 122) # Combination of prior information and trial results -As outlined in Fleischer et al. [Bayesian MCPMod. Pharmaceutical Statistics. 2022; 21(3): 654-670.], -in a first step the posterior is calculated combining the prior information with -the estimated results of the new trial. Via the summary function it is possible -to print out the summary information of the posterior distributions. +As outlined in [@fleischer_2022], in a first step the posterior is calculated +combining the prior information with the estimated results of the new trial. + +Via the summary function it is possible to print out the summary information of +the posterior distributions. ```{r Trial results} posterior <- getPosterior( @@ -209,7 +219,7 @@ the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled by the specified alpha level. A pseudo-optimal contrast matrix is generated based on the variability of the -posterior distribution (see Fleischer et al. 2022 for more details). +posterior distribution (see [@fleischer_2022] for more details). ```{r Preparation of input for Bayesian MCPMod Test step} crit_pval <- getCritProb( @@ -273,7 +283,8 @@ is approximated and reduced by a one-dimensional normal distribution. The actual fit (on this approximated posterior distribution) is then performed using generalized least squares criterion. In contrast, for the full fit, the -non-linear optimization problem is addressed via the Nelder Mead algorithm. +non-linear optimization problem is addressed via the Nelder Mead algorithm +[@neldermead_2024]. The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. @@ -296,7 +307,7 @@ fit <- getModelFits( simple = FALSE) ``` -Via the predict() function, one can also receive estimates for dose levels that +Via the `predict()` function, one can also receive estimates for dose levels that were not included in the trial. ```{r Predict} @@ -326,7 +337,7 @@ plot(fit, cr_bands = TRUE) ``` The bootstrap based quantiles can also be directly calculated via the -getBootstrapQuantiles() function. +`getBootstrapQuantiles()` function. For this example, only 6 quantiles are bootstrapped for each model fit. @@ -348,7 +359,7 @@ based 50% quantile shows the median fit of the random sampling and fitting proce # Additional note It is also possible to perform the testing and modeling step in a combined fashion -via the performBayesianMCPMod() function. +via the `performBayesianMCPMod()` function. This code serves merely as an example and is not run in this vignette. diff --git a/vignettes/references.bib b/vignettes/references.bib new file mode 100644 index 0000000..3e801c8 --- /dev/null +++ b/vignettes/references.bib @@ -0,0 +1,25 @@ +@article{fleischer_2022, + title={Bayesian MCPMod}, + author={Fleischer F, Bossert S, Deng Q, Loley C, and Gierse J}, + journal={Pharmaceutical Statistics}, + volume={21}, + number={3}, + pages={654--670}, + year={2022} +} + +@misc {neldermead_2024a, + author = "Wikipedia", + title = "Nelder-Mead method", + year = "2024", + url = "https://en.wikipedia.org/wiki/Nelder-Mead_method", + note = " [Online; accessed 25-February-2024]" +} + +@misc {nct00735709_2024a, + author = "ClinicalTrials.gov", + title = "NCT00735709", + year = "2024", + url = "https://clinicaltrials.gov/study/NCT00735709?term=NCT00735709&rank=1", + note = " [Online; accessed 25-February-2024]" +} From bba4649badc51169279ceab969caea00b2fb946e Mon Sep 17 00:00:00 2001 From: "Brooks,Steven (MED BDS) BII-CN-S" Date: Sun, 25 Feb 2024 17:09:34 +0800 Subject: [PATCH 03/49] made prior spec more generic --- vignettes/analysis_normal.Rmd | 283 ++++++++++++++++++++++++++++++++++ vignettes/analysis_normal.qmd | 172 +++++++-------------- 2 files changed, 335 insertions(+), 120 deletions(-) create mode 100644 vignettes/analysis_normal.Rmd diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd new file mode 100644 index 0000000..b615645 --- /dev/null +++ b/vignettes/analysis_normal.Rmd @@ -0,0 +1,283 @@ +--- +title: "Analysis Example of Bayesian MCPMod for Continuous Data" +output: rmarkdown::html_vignette +number_sections: true +vignette: > + %\VignetteIndexEntry{Analysis Example of Bayesian MCPMod for Continuous Data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +```{r setup} +library(BayesianMCPMod) +library(clinDR) +library(dplyr) + +set.seed(7015) +``` + +# Background and data + +In this vignette we will show the use of the Bayesian MCPMod package for continuous distributed data. +The focus lies on the utilization of an informative prior and the Bayesian MCPMod evaluation of a single trial. +We will use data that is included in the clinDR package. +More specifically, trial results of BRINTELLIX will be used to illustrate the specification of an informative prior and the usage of such a prior for the Bayesian evaluation of a (hypothetical) new trial. +BRINTELLIX is a medication used to treat major depressive disorder. +Various clinical trials with different dose groups, including control groups, were conducted. + +# Calculation of a MAP prior + +In a first step, a meta analytic prior will be calculated using historical data from 4 trials with main endpoint Change from baseline in MADRS score after 8 weeks. +Please note that only information from the control group will be integrated leading to an informative mixture prior for the control group, while for the active groups a non-informative prior will be specified. +```{r Historical Data for Control Arm} +data("metaData") +dataset <- filter(as.data.frame(metaData), bname == "BRINTELLIX") +histcontrol <- filter( + dataset, + dose == 0, + primtime == 8, + indication == "MAJOR DEPRESSIVE DISORDER", + protid != 5) + +hist_data <- data.frame( + trial = histcontrol$nctno, + est = histcontrol$rslt, + se = histcontrol$se, + sd = histcontrol$sd, + n = histcontrol$sampsize) +``` +Here, we suggest a function to construct a list of prior distributions for the different dose groups. +This function is adapted to the needs of this example. +Other applications may need a different way to construct prior distributions. +```{r Defining MAP prior function} +getPriorList <- function ( + + hist_data, + dose_levels, + dose_names = NULL, + robust_weight = 0.5 + +) { + + sd_tot <- with(hist_data, sum(sd * n) / sum(n)) + + gmap <- RBesT::gMAP( + formula = cbind(est, se) ~ 1 | trial, + weights = hist_data$n, + data = hist_data, + family = gaussian, + beta.prior = cbind(0, 100 * sd_tot), + tau.dist = "HalfNormal", + tau.prior = cbind(0, sd_tot / 4)) + + prior_ctr <- RBesT::automixfit(gmap) + + if (!is.null(robust_weight)) { + + prior_ctr <- suppressMessages(RBesT::robustify( + priormix = prior_ctr, + weight = robust_weight, + sigma = sd_tot)) + + } + + prior_trt <- RBesT::mixnorm( + comp1 = c(w = 1, m = summary(prior_ctr)[1], n = 1), + sigma = sd_tot, + param = "mn") + + prior_list <- c(list(prior_ctr), + rep(x = list(prior_trt), + times = length(dose_levels[-1]))) + + if (is.null(dose_names)) { + + dose_names <- c("Ctr", paste0("DG_", seq_along(dose_levels[-1]))) + + } + + names(prior_list) <- dose_names + + return (prior_list) + +} +``` +With the dose levels to be investigated, the prior distribution can be constructed. +```{r Getting the MAP prior} +dose_levels <- c(0, 2.5, 5, 10) + +prior_list <- getPriorList( + hist_data = hist_data, + dose_levels = dose_levels, + robust_weight = 0.3) + +BayesianMCPMod::getESS(prior_list) +``` + +# Specifications for the new trial + +To be able to apply the Bayesian MCPMod approach, candidate models need to be specified using functions from the R package DoseFinding. +Since there are only 3 active dose levels we will limit the set of candidate models to a linear, an exponential, and an emax model. +Note that the linear candidate model does not require a guesstimate. +```{r Pre-Specification of candidate models} +exp_guesst <- DoseFinding::guesst( + d = 5, + p = c(0.2), + model = "exponential", + Maxd = max(dose_levels)) + +emax_guesst <- DoseFinding::guesst( + d = 2.5, + p = c(0.9), + model = "emax") + +mods <- DoseFinding::Mods( + linear = NULL, + emax = emax_guesst, + exponential = exp_guesst, + doses = dose_levels, + maxEff = -1, + placEff = -12.8) +``` +We will use the trial with ct.gov number NCT00735709 as exemplary new trial. +```{r new trial} +new_trial <- filter( + dataset, + primtime == 8, + indication == "MAJOR DEPRESSIVE DISORDER", + protid == 5) + +n_patients <- c(128, 124, 129, 122) +``` +# Combination of prior information and trial results +As outlined in Fleischer et al. [Bayesian MCPMod. Pharmaceutical Statistics. 2022; 21(3): 654-670.], in a first step the posterior is calculated combining the prior information with the estimated results of the new trial. Via the summary function it is possible to print out the summary information of the posterior distributions. +```{r Trial results} +posterior <- getPosterior( + prior = prior_list, + mu_hat = new_trial$rslt, + se_hat = new_trial$se, + calc_ess = TRUE) + +summary(posterior) +``` +# Execution of Bayesian MCPMod Test step + +For the execution of the testing step of Bayesian MCPMod a critical value on the probability scale will be determined for a given alpha level. +This critical value is calculated using the re-estimated contrasts for the frequentist MCPMod to ensure that, when using non-informative priors, the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled by the specified alpha level. + +A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see Fleischer et al. 2022 for more details). +```{r Preparation of input for Bayesian MCPMod Test step} +crit_pval <- getCritProb( + mods = mods, + dose_levels = dose_levels, + se_new_trial = new_trial$se, + alpha_crit_val = 0.05) + +contr_mat <- getContr( + mods = mods, + dose_levels = dose_levels, + sd_posterior = summary(posterior)[, 2]) +``` +Please note that there are different ways to derive the contrasts. +The following code shows the implementation of some of these ways but it is not executed and the contrast specification above is used. +```{r , eval = FALSE} +# i) the frequentist contrast +contr_mat_prior <- getContr( + mods = mods, + dose_levels = dose_levels, + dose_weights = n_patients, + prior_list = prior_list) +# ii) re-estimated frequentist contrasts +contr_mat_prior <- getContr( + mods = mods, + dose_levels = dose_levels, + se_new_trial = new_trial$se) +# iii) Bayesian approach using number of patients for new trial and prior distribution +contr_mat_prior <- getContr( + mods = mods, + dose_levels = dose_levels, + dose_weights = n_patients, + prior_list = prior_list) +``` +The Bayesian MCP testing step is then executed based on the posterior information, the provided contrasts and the multiplicity adjusted critical value. +```{r Execution of Bayesian MCPMod Test step} +BMCP_result <- performBayesianMCP( + posterior_list = posterior, + contr = contr_mat, + crit_prob_adj = crit_pval) + +BMCP_result +``` +The testing step is significant indicating a non-flat dose-response shape. +In detail, all 3 models are significant and the p-value for the emax model indicates deviation from the null hypothesis the most. + +# Model fitting and visualization of results + +In the model fitting step the posterior distribution is used as basis. +Both simplified and full fitting are performed. +For the simplified fit, the multivariate normal distribution of the control group is approximated and reduced by a one-dimensional normal distribution. +The actual fit (on this approximated posterior distribution) is then performed using generalized least squares criterion. +In contrast, for the full fit, the non-linear optimization problem is addressed via the Nelder Mead algorithm. + +The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. +For the considered case, the simplified and the full fit are very similar. +```{r Model fitting} +# Option a) Simplified approach by using approximated posterior distribution +fit_simple <- getModelFits( + models = names(mods), + dose_levels = dose_levels, + posterior = posterior, + simple = TRUE) + +# Option b) Making use of the complete posterior distribution +fit <- getModelFits( + models = names(mods), + dose_levels = dose_levels, + posterior = posterior, + simple = FALSE) +``` +Via the predict() function, one can also receive estimates for dose levels that were not included in the trial. +```{r Predict} +predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) +``` +It is possible to plot the fitted dose response models and an AIC based average model (black lines). +```{r Plot simple vs fit} +plot(fit_simple) +plot(fit) +``` +To assess the uncertainty, one can additionally visualize credible bands (orange shaded areas, default levels are 50% and 95%). +These credible bands are calculated with a bootstrap method as follows: +Samples from the posterior distribution are drawn and for every sample the simplified fitting step and a prediction is performed. +These predictions are then used to identify and visualize the specified quantiles. +```{r Plot with bootstrap} +plot(fit, cr_bands = TRUE) +``` +The bootstrap based quantiles can also be directly calculated via the getBootstrapQuantiles() function. +For this example, only 6 quantiles are bootstrapped for each model fit. +```{r Bootstrap} +getBootstrapQuantiles( + model_fits = fit, + quantiles = c(0.025, 0.5, 0.975), + doses = c(0, 2.5, 4, 5, 7, 10), + n_samples = 6) +``` +Technical note: The median quantile of the bootstrap based procedure is not necessary similar to the main model fit, as they are derived via different procedures. +The main fit, i.e. the black lines in the plot, show the best fit of a certain model based on minimizing the residuals for the posterior distribution, while the bootstrap based 50% quantile shows the median fit of the random sampling and fitting procedure. + +# Additional note +It is also possible to perform the testing and modeling step in a combined fashion via the performBayesianMCPMod() function. +This code serves merely as an example and is not run in this vignette. +```{r, eval = FALSE} +performBayesianMCPMod( + posterior_list = posterior, + contr = contr_mat, + crit_prob_adj = crit_pval, + simple = FALSE) +``` \ No newline at end of file diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index a08320c..8eb7d38 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -16,17 +16,20 @@ vignette: > %\VignetteEncoding{UTF-8} --- -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` +# ```{r, include = FALSE} +# knitr::opts_chunk$set( +# collapse = TRUE, +# comment = "#>" +# ) +# ``` ```{r setup} library(BayesianMCPMod) +library(parallelly) library(clinDR) -library(dplyr) +library(tidyverse) + +options(mc.cores = max(1, parallelly::availableCores() - 2)) set.seed(7015) ``` @@ -36,114 +39,34 @@ set.seed(7015) In this vignette we will show the use of the Bayesian MCPMod package for continuous distributed data. -The focus lies on the utilization of an informative prior and the Bayesian MCPMod -evaluation of a single trial. We will use data that is included in the clinDR package. -More specifically, trial results of BRINTELLIX will be used to illustrate the -specification of an informative prior and the usage of such a prior for the -Bayesian evaluation of a (hypothetical) new trial. - -BRINTELLIX is a medication used to treat major depressive disorder. -Various clinical trials with different dose groups, including control groups, -were conducted. - -# Calculation of a MAP prior +Priors can be grounded in historical data, leveraging the `{RBesT}` package functions +to construct the informative priors. This approach allows for the synthesis of prior +knowledge with current data, enhancing the accuracy of trial evaluations. -In a first step, a meta analytic prior will be calculated using historical data -from 4 trials with main endpoint Change from baseline in MADRS score after 8 weeks. +The focus of this vignette is more generic, however. We utilize a non-informative +prior for the Bayesian MCPMod evaluation of a single trial. -Please note that only information from the control group will be integrated leading -to an informative mixture prior for the control group, while for the active groups -a non-informative prior will be specified. - -```{r Historical Data for Control Arm} -data("metaData") -dataset <- filter(as.data.frame(metaData), bname == "BRINTELLIX") -histcontrol <- filter( - dataset, - dose == 0, - primtime == 8, - indication == "MAJOR DEPRESSIVE DISORDER", - protid != 5) - -hist_data <- data.frame( - trial = histcontrol$nctno, - est = histcontrol$rslt, - se = histcontrol$se, - sd = histcontrol$sd, - n = histcontrol$sampsize) -``` +# Non-Informative Prior Specification -Here, we suggest a function to construct a list of prior distributions for the -different dose groups. - -This function is adapted to the needs of this example. -Other applications may need a different way to construct prior distributions. - -```{r Defining MAP prior function} -getPriorList <- function ( - - hist_data, - dose_levels, - dose_names = NULL, - robust_weight = 0.5 - -) { - - sd_tot <- with(hist_data, sum(sd * n) / sum(n)) - - gmap <- RBesT::gMAP( - formula = cbind(est, se) ~ 1 | trial, - weights = hist_data$n, - data = hist_data, - family = gaussian, - beta.prior = cbind(0, 100 * sd_tot), - tau.dist = "HalfNormal", - tau.prior = cbind(0, sd_tot / 4)) - - prior_ctr <- RBesT::automixfit(gmap) - - if (!is.null(robust_weight)) { - - prior_ctr <- suppressMessages(RBesT::robustify( - priormix = prior_ctr, - weight = robust_weight, - sigma = sd_tot)) - - } - - prior_trt <- RBesT::mixnorm( - comp1 = c(w = 1, m = summary(prior_ctr)[1], n = 1), - sigma = sd_tot, - param = "mn") - - prior_list <- c(list(prior_ctr), - rep(x = list(prior_trt), - times = length(dose_levels[-1]))) - - if (is.null(dose_names)) { - - dose_names <- c("Ctr", paste0("DG_", seq_along(dose_levels[-1]))) - - } - - names(prior_list) <- dose_names - - return (prior_list) - -} -``` +Rather than constructing a complex mixture prior for the control group and +differentiating active groups with detailed priors, we will specify non-informative +priors across all dose groups. This simplification aims to underscore the +flexibility in prior selection and its impact on the Bayesian analysis framework. -With the dose levels to be investigated, the prior distribution can be constructed. +Non-informative priors, often chosen for their neutrality, offer a baseline +from which the trial data can speak more prominently. This approach is particularly +useful in early-stage research or when historical data is limited or not directly applicable. -```{r Getting the MAP prior} +```{r Non-Informative Prior Specification} +# Specifying non-informative priors for all dose groups dose_levels <- c(0, 2.5, 5, 10) -prior_list <- getPriorList( - hist_data = hist_data, - dose_levels = dose_levels, - robust_weight = 0.3) +prior_list <- lapply(dose_levels, function(dose_group) { + #TODO: make a uniform prior from -100 to 100 + RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200)) # weakly informative normal distribution +}) -getESS(prior_list) +names(prior_list) <- dose_level_names ``` # Specifications for the new trial @@ -161,12 +84,14 @@ exp_guesst <- DoseFinding::guesst( d = 5, p = c(0.2), model = "exponential", - Maxd = max(dose_levels)) + Maxd = max(dose_levels) +) emax_guesst <- DoseFinding::guesst( d = 2.5, p = c(0.9), - model = "emax") + model = "emax" +) mods <- DoseFinding::Mods( linear = NULL, @@ -174,18 +99,23 @@ mods <- DoseFinding::Mods( exponential = exp_guesst, doses = dose_levels, maxEff = -1, - placEff = -12.8) + placEff = -12.8 +) ``` We will use the trial with ct.gov number NCT00735709 as exemplary new trial [@nct00735709_2024a]. ```{r new trial} -new_trial <- filter( +data("metaData") +dataset <- dplyr::filter(tibble::tibble(metaData), bname == "BRINTELLIX") + +new_trial <- dplyr::filter( dataset, - primtime == 8, + primtime == 8, indication == "MAJOR DEPRESSIVE DISORDER", - protid == 5) + protid == 5 +) n_patients <- c(128, 124, 129, 122) ``` @@ -199,11 +129,11 @@ Via the summary function it is possible to print out the summary information of the posterior distributions. ```{r Trial results} -posterior <- getPosterior( - prior = prior_list, - mu_hat = new_trial$rslt, - se_hat = new_trial$se, - calc_ess = TRUE) +posterior <- BayesianMCPMod::getPosterior( + prior_list = prior_list, + mu_hat = new_trial$rslt, + se_hat = new_trial$se +) summary(posterior) ``` @@ -226,12 +156,14 @@ crit_pval <- getCritProb( mods = mods, dose_levels = dose_levels, se_new_trial = new_trial$se, - alpha_crit_val = 0.05) + alpha_crit_val = 0.05 +) contr_mat <- getContr( mods = mods, dose_levels = dose_levels, - sd_posterior = summary(posterior)[, 2]) + sd_posterior = summary(posterior)[, 2] +) ``` Please note that there are different ways to derive the contrasts. From c497027a4db9c0a4d860138486365ea652f6f6cf Mon Sep 17 00:00:00 2001 From: "Brooks,Steven (MED BDS) BII-CN-S" Date: Sun, 25 Feb 2024 18:03:52 +0800 Subject: [PATCH 04/49] working on dose models; improved error message for getOptParams() --- R/modelling.R | 2 +- vignettes/analysis_normal.qmd | 67 ++++++++++++------ ...re-Specification of candidate models-1.png | Bin 0 -> 131179 bytes 3 files changed, 48 insertions(+), 21 deletions(-) create mode 100644 vignettes/analysis_normal_files/figure-html/Pre-Specification of candidate models-1.png diff --git a/R/modelling.R b/R/modelling.R index d52da7f..7007310 100644 --- a/R/modelling.R +++ b/R/modelling.R @@ -148,7 +148,7 @@ getModelFitOpt <- function ( ub <- c(Inf, Inf, 1.5 * max(dose_levels), 0.5 * max(dose_levels)) expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] / (1 + exp((theta[3] - dose_levels) / theta[4]))))^2 / (post_combs$vars[i, ])))}, { - stop ("error")}) + stop ("error: model shape not yet implemented")}) simple_fit <- getModelFitSimple( model = model, diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 8eb7d38..c38f27f 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -15,14 +15,14 @@ vignette: > %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} --- - + # ```{r, include = FALSE} # knitr::opts_chunk$set( # collapse = TRUE, # comment = "#>" # ) # ``` - + ```{r setup} library(BayesianMCPMod) library(parallelly) @@ -32,6 +32,12 @@ library(tidyverse) options(mc.cores = max(1, parallelly::availableCores() - 2)) set.seed(7015) + +# source all R files in R/ (load custom functions) +purrr::walk( + list.files("R", pattern = "*.R", full.names = TRUE), + source +) ``` # Background and data @@ -66,7 +72,7 @@ prior_list <- lapply(dose_levels, function(dose_group) { RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200)) # weakly informative normal distribution }) -names(prior_list) <- dose_level_names +names(prior_list) <- c("Ctr", paste0("DG_", dose_levels[-1])) ``` # Specifications for the new trial @@ -81,26 +87,47 @@ Note that the linear candidate model does not require a guesstimate. ```{r Pre-Specification of candidate models} exp_guesst <- DoseFinding::guesst( - d = 5, - p = c(0.2), - model = "exponential", - Maxd = max(dose_levels) + model = "exponential", + d = 5, p = 0.2, Maxd = max(dose_levels) ) - emax_guesst <- DoseFinding::guesst( - d = 2.5, - p = c(0.9), - model = "emax" + model = "emax", + d = 2.5, p = 0.9 +) +sigEmax_guesst <- DoseFinding::guesst( + model = "sigEmax", + d = c(2.5, 5), p = c(0.5, 0.95) +) +logistic_guesst <- DoseFinding::guesst( + model = "logistic", + d = c(5, 10), p = c(0.1, 0.85) +) +# TODO: figure out how to paramaterize a beta +# beta_guesst <- DoseFinding::guesst( +# model = "betaMod", +# d = 0.4, p = 0.8, dMax = 0.8, Maxd = 1, scal = 1.2 +# ) +quadratic_guesst <- DoseFinding::guesst( + model = "quadratic", + d = 5, p = 0.9 ) mods <- DoseFinding::Mods( linear = NULL, - emax = emax_guesst, exponential = exp_guesst, + emax = emax_guesst, + sigEmax = sigEmax_guesst, + # TODO: need to implement linlog shape in getOptParams() + # linlog = NULL, + logistic = logistic_guesst, + # beta = beta_guesst, + quadratic = quadratic_guesst, doses = dose_levels, maxEff = -1, placEff = -12.8 ) + +plot(mods) ``` We will use the trial with ct.gov number NCT00735709 as exemplary new trial @@ -135,7 +162,7 @@ posterior <- BayesianMCPMod::getPosterior( se_hat = new_trial$se ) -summary(posterior) +knitr::kable(summary(posterior)) ``` # Execution of Bayesian MCPMod Test step @@ -215,8 +242,8 @@ is approximated and reduced by a one-dimensional normal distribution. The actual fit (on this approximated posterior distribution) is then performed using generalized least squares criterion. In contrast, for the full fit, the -non-linear optimization problem is addressed via the Nelder Mead algorithm -[@neldermead_2024]. +non-linear optimization problem is addressed via the Nelder-Mead algorithm +[@neldermead_2024] implemented by the `{nloptr}` package. The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. @@ -225,7 +252,7 @@ For the considered case, the simplified and the full fit are very similar. ```{r Model fitting} # Option a) Simplified approach by using approximated posterior distribution -fit_simple <- getModelFits( +fit_simple <- BayesianMCPMod::getModelFits( models = names(mods), dose_levels = dose_levels, posterior = posterior, @@ -297,8 +324,8 @@ This code serves merely as an example and is not run in this vignette. ```{r, eval = FALSE} performBayesianMCPMod( - posterior_list = posterior, - contr = contr_mat, - crit_prob_adj = crit_pval, - simple = FALSE) + posterior_list = posterior, + contr = contr_mat, + crit_prob_adj = crit_pval, + simple = FALSE) ``` diff --git a/vignettes/analysis_normal_files/figure-html/Pre-Specification of candidate models-1.png b/vignettes/analysis_normal_files/figure-html/Pre-Specification of candidate models-1.png new file mode 100644 index 0000000000000000000000000000000000000000..36db7845e4eca69a69e61a47db1896291cfcf9a6 GIT binary patch literal 131179 zcmeFZWmJ@H|27H;QYr?BMVCV=64IcAba$zAcQZ4fAc_Lg($d}CDj=nFr*wA=F|p6N zZ++f(-|us;{c-QL_Wy&+Sx#K%bzZ+Xe#dc~zEe?>xlD401P2G_vg|WSH5?oQaU2}H z&qNo&zvwNE^5fv(U$J`nRK@10%u@$j2WNH17ba#hX7*;zRwimPPjGM^hlFSvTHezn z7fq-wXJhPnJ(QIdaRtpARbi{vA26|sHY>NAVvn?~koefSIh|z1{AdB0VFeK-m$>>h z=U3z}jQ&>^I_vzc_Z(>zDrxwy@1OLY$)pZY5^vN#DzA^Mv1#m{I$ciOfvwEAY|oH> ztYO!WnPI|Xzf;7qG&o=0zTDa>==v4fcNyBp!4?|L8qJJ*ci~&}#MTb&_nFb-Y6Qk& zqew*t_w97xSi$2i`gt=PG(}sPp&!1)c?a?6%M;>OA=^aS`Rgr}ING<8$#%pIPw28c87j=1QwX8&mg?lok5^(CYDpz>e{8WeHWMmg^N^{DcC6!2; 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+- vignettes/analysis_normal.Rmd | 6 +- vignettes/analysis_normal.qmd | 82 +++++++++--------- ...fit_0930d2741c792c1cfe5e652692265f4b.RData | Bin 0 -> 2874 bytes ...s fit_0930d2741c792c1cfe5e652692265f4b.rdb | 0 ...s fit_0930d2741c792c1cfe5e652692265f4b.rdx | Bin 0 -> 125 bytes ...rap_ccbd6525747bdbad34a7cea52543fa05.RData | Bin 0 -> 2874 bytes ...strap_ccbd6525747bdbad34a7cea52543fa05.rdb | 0 ...strap_ccbd6525747bdbad34a7cea52543fa05.rdx | Bin 0 -> 125 bytes .../analysis_normal_cache/html/__packages | 16 ++++ .../figure-html/Plot simple vs fit-1.png | Bin 0 -> 95169 bytes .../figure-html/Plot simple vs fit-2.png | Bin 0 -> 95169 bytes .../figure-html/Plot with bootstrap-1.png | Bin 0 -> 145647 bytes vignettes/references.bib | 2 +- 14 files changed, 60 insertions(+), 48 deletions(-) create mode 100644 vignettes/analysis_normal_cache/html/Plot simple vs fit_0930d2741c792c1cfe5e652692265f4b.RData create mode 100644 vignettes/analysis_normal_cache/html/Plot simple vs fit_0930d2741c792c1cfe5e652692265f4b.rdb create mode 100644 vignettes/analysis_normal_cache/html/Plot simple vs fit_0930d2741c792c1cfe5e652692265f4b.rdx create mode 100644 vignettes/analysis_normal_cache/html/Plot with bootstrap_ccbd6525747bdbad34a7cea52543fa05.RData create mode 100644 vignettes/analysis_normal_cache/html/Plot with bootstrap_ccbd6525747bdbad34a7cea52543fa05.rdb create mode 100644 vignettes/analysis_normal_cache/html/Plot with bootstrap_ccbd6525747bdbad34a7cea52543fa05.rdx create mode 100644 vignettes/analysis_normal_cache/html/__packages create mode 100644 vignettes/analysis_normal_files/figure-html/Plot simple vs fit-1.png create mode 100644 vignettes/analysis_normal_files/figure-html/Plot simple vs fit-2.png create mode 100644 vignettes/analysis_normal_files/figure-html/Plot with bootstrap-1.png diff --git a/R/modelling.R b/R/modelling.R index 7007310..2ba5933 100644 --- a/R/modelling.R +++ b/R/modelling.R @@ -186,7 +186,7 @@ getModelFitOpt <- function ( param_list <- getOptParams(model, dose_levels, posterior) post_combs <- getPostCombsI(posterior) - + fit <- nloptr::nloptr( eval_f = optFun, x0 = param_list$params$x0, diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd index b615645..3619620 100644 --- a/vignettes/analysis_normal.Rmd +++ b/vignettes/analysis_normal.Rmd @@ -1,11 +1,7 @@ --- title: "Analysis Example of Bayesian MCPMod for Continuous Data" -output: rmarkdown::html_vignette +output: html_document number_sections: true -vignette: > - %\VignetteIndexEntry{Analysis Example of Bayesian MCPMod for Continuous Data} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index c38f27f..f231ae8 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -16,28 +16,17 @@ vignette: > %\VignetteEncoding{UTF-8} --- -# ```{r, include = FALSE} -# knitr::opts_chunk$set( -# collapse = TRUE, -# comment = "#>" -# ) -# ``` - ```{r setup} +#| message: false +#| include: false + library(BayesianMCPMod) -library(parallelly) +library(RBesT) library(clinDR) -library(tidyverse) - -options(mc.cores = max(1, parallelly::availableCores() - 2)) +library(dplyr) +library(tibble) set.seed(7015) - -# source all R files in R/ (load custom functions) -purrr::walk( - list.files("R", pattern = "*.R", full.names = TRUE), - source -) ``` # Background and data @@ -63,12 +52,13 @@ Non-informative priors, often chosen for their neutrality, offer a baseline from which the trial data can speak more prominently. This approach is particularly useful in early-stage research or when historical data is limited or not directly applicable. +[TODO: make a uniform prior from -100 to 100]{.aside} + ```{r Non-Informative Prior Specification} # Specifying non-informative priors for all dose groups dose_levels <- c(0, 2.5, 5, 10) prior_list <- lapply(dose_levels, function(dose_group) { - #TODO: make a uniform prior from -100 to 100 RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200)) # weakly informative normal distribution }) @@ -85,6 +75,10 @@ models to a linear, an exponential, and an emax model. Note that the linear candidate model does not require a guesstimate. +[TODO: figure out how to paramaterize a beta]{.aside} + +[TODO: need to implement linlog shape in getOptParams()]{.aside} + ```{r Pre-Specification of candidate models} exp_guesst <- DoseFinding::guesst( model = "exponential", @@ -102,7 +96,6 @@ logistic_guesst <- DoseFinding::guesst( model = "logistic", d = c(5, 10), p = c(0.1, 0.85) ) -# TODO: figure out how to paramaterize a beta # beta_guesst <- DoseFinding::guesst( # model = "betaMod", # d = 0.4, p = 0.8, dMax = 0.8, Maxd = 1, scal = 1.2 @@ -117,7 +110,6 @@ mods <- DoseFinding::Mods( exponential = exp_guesst, emax = emax_guesst, sigEmax = sigEmax_guesst, - # TODO: need to implement linlog shape in getOptParams() # linlog = NULL, logistic = logistic_guesst, # beta = beta_guesst, @@ -131,7 +123,7 @@ plot(mods) ``` We will use the trial with ct.gov number NCT00735709 as exemplary new trial -[@nct00735709_2024a]. +[@nct00735709_2024a]. This dataset comes from the `{clinDR}` package. ```{r new trial} data("metaData") @@ -179,14 +171,14 @@ A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see [@fleischer_2022] for more details). ```{r Preparation of input for Bayesian MCPMod Test step} -crit_pval <- getCritProb( +crit_pval <- BayesianMCPMod::getCritProb( mods = mods, dose_levels = dose_levels, se_new_trial = new_trial$se, alpha_crit_val = 0.05 ) -contr_mat <- getContr( +contr_mat <- BayesianMCPMod::getContr( mods = mods, dose_levels = dose_levels, sd_posterior = summary(posterior)[, 2] @@ -199,18 +191,18 @@ executed and the contrast specification above is used. ```{r , eval = FALSE} # i) the frequentist contrast -contr_mat_prior <- getContr( +contr_mat_prior <- BayesianMCPMod::getContr( mods = mods, dose_levels = dose_levels, dose_weights = n_patients, prior_list = prior_list) # ii) re-estimated frequentist contrasts -contr_mat_prior <- getContr( +contr_mat_prior <- BayesianMCPMod::getContr( mods = mods, dose_levels = dose_levels, se_new_trial = new_trial$se) # iii) Bayesian approach using number of patients for new trial and prior distribution -contr_mat_prior <- getContr( +contr_mat_prior <- BayesianMCPMod::getContr( mods = mods, dose_levels = dose_levels, dose_weights = n_patients, @@ -221,7 +213,7 @@ The Bayesian MCP testing step is then executed based on the posterior informatio the provided contrasts and the multiplicity adjusted critical value. ```{r Execution of Bayesian MCPMod Test step} -BMCP_result <- performBayesianMCP( +BMCP_result <- BayesianMCPMod::performBayesianMCP( posterior_list = posterior, contr = contr_mat, crit_prob_adj = crit_pval) @@ -243,13 +235,15 @@ is approximated and reduced by a one-dimensional normal distribution. The actual fit (on this approximated posterior distribution) is then performed using generalized least squares criterion. In contrast, for the full fit, the non-linear optimization problem is addressed via the Nelder-Mead algorithm -[@neldermead_2024] implemented by the `{nloptr}` package. +[@neldermead_2024a] implemented by the `{nloptr}` package. The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. For the considered case, the simplified and the full fit are very similar. +[TODO: Debug error in getModelFits: 'Error in is.nloptr(ret) : at least one element in x0 > ub']{.aside} + ```{r Model fitting} # Option a) Simplified approach by using approximated posterior distribution fit_simple <- BayesianMCPMod::getModelFits( @@ -259,24 +253,27 @@ fit_simple <- BayesianMCPMod::getModelFits( simple = TRUE) # Option b) Making use of the complete posterior distribution -fit <- getModelFits( - models = names(mods), - dose_levels = dose_levels, - posterior = posterior, - simple = FALSE) +# fit <- BayesianMCPMod::getModelFits( +# models = names(mods), +# dose_levels = dose_levels, +# posterior = posterior, +# simple = FALSE) +fit <- fit_simple ``` -Via the `predict()` function, one can also receive estimates for dose levels that +Via the `stats::predict()` function, one can also receive estimates for dose levels that were not included in the trial. ```{r Predict} -predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) +stats::predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) +stats::predict(fit_simple, doses = c(0, 2.5, 4, 5, 7, 10)) ``` It is possible to plot the fitted dose response models and an AIC based average model (black lines). ```{r Plot simple vs fit} +#| cache: true plot(fit_simple) plot(fit) ``` @@ -292,6 +289,7 @@ simplified fitting step and a prediction is performed. - These predictions are then used to identify and visualize the specified quantiles. ```{r Plot with bootstrap} +#| cache: true plot(fit, cr_bands = TRUE) ``` @@ -301,7 +299,7 @@ The bootstrap based quantiles can also be directly calculated via the For this example, only 6 quantiles are bootstrapped for each model fit. ```{r Bootstrap} -getBootstrapQuantiles( +BayesianMCPMod::getBootstrapQuantiles( model_fits = fit, quantiles = c(0.025, 0.5, 0.975), doses = c(0, 2.5, 4, 5, 7, 10), @@ -322,10 +320,12 @@ via the `performBayesianMCPMod()` function. 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0 HcmV?d00001 diff --git a/vignettes/references.bib b/vignettes/references.bib index 3e801c8..d6195b7 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -1,6 +1,6 @@ @article{fleischer_2022, title={Bayesian MCPMod}, - author={Fleischer F, Bossert S, Deng Q, Loley C, and Gierse J}, + author={Fleischer F, Bossert S, Deng Q, Loley C, Gierse J}, journal={Pharmaceutical Statistics}, volume={21}, number={3}, From c6898a0e5bcfc61b0e5b4f415a0a3d6f76d4ddc7 Mon Sep 17 00:00:00 2001 From: "Brooks,Steven (MED BDS) BII-CN-S" Date: Sun, 25 Feb 2024 18:33:10 +0800 Subject: [PATCH 06/49] edit gitignore --- vignettes/.gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/vignettes/.gitignore b/vignettes/.gitignore index 097b241..45d081a 100644 --- a/vignettes/.gitignore +++ b/vignettes/.gitignore @@ -1,2 +1,3 @@ *.html *.R +_cache/* \ No newline at end of file From 3b11a21a1dbb38de4292f0460909199d5d15791d Mon Sep 17 00:00:00 2001 From: "Brooks,Steven (MED BDS) BII-CN-S" Date: Sun, 25 Feb 2024 18:33:26 +0800 Subject: [PATCH 07/49] edit gitignore --- vignettes/.gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/vignettes/.gitignore b/vignettes/.gitignore index 45d081a..1390e64 100644 --- a/vignettes/.gitignore +++ b/vignettes/.gitignore @@ -1,3 +1,4 @@ *.html *.R -_cache/* \ No newline at end of file +_cache/* +_files/* \ No newline at end of file From 2d0bcdf038a06ef48b307d115cf7567a666340fc Mon Sep 17 00:00:00 2001 From: "Brooks,Steven (MED BDS) BII-CN-S" Date: Sun, 25 Feb 2024 18:34:10 +0800 Subject: [PATCH 08/49] rm quarto render stuff --- ... fit_0930d2741c792c1cfe5e652692265f4b.RData | Bin 2874 -> 0 bytes ...vs fit_0930d2741c792c1cfe5e652692265f4b.rdb | 0 ...vs fit_0930d2741c792c1cfe5e652692265f4b.rdx | Bin 125 -> 0 bytes ...trap_ccbd6525747bdbad34a7cea52543fa05.RData | Bin 2874 -> 0 bytes ...tstrap_ccbd6525747bdbad34a7cea52543fa05.rdb | 0 ...tstrap_ccbd6525747bdbad34a7cea52543fa05.rdx | Bin 125 -> 0 bytes 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2 deletions(-) diff --git a/vignettes/.gitignore b/vignettes/.gitignore index 1390e64..cc5d5f5 100644 --- a/vignettes/.gitignore +++ b/vignettes/.gitignore @@ -1,4 +1,4 @@ *.html *.R -_cache/* -_files/* \ No newline at end of file +*_cache/* +*_files/* \ No newline at end of file From 77697a5f05f606640af5a657b59fd52f4e8fd1a4 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sat, 2 Mar 2024 14:40:27 +0800 Subject: [PATCH 10/49] added missing dose shapes --- DESCRIPTION | 2 +- vignettes/.gitignore | 3 +- vignettes/Simulation_Example.Rmd | 163 ------------------ vignettes/analysis_normal.Rmd | 279 ------------------------------- vignettes/analysis_normal.qmd | 14 +- 5 files changed, 6 insertions(+), 455 deletions(-) delete mode 100644 vignettes/Simulation_Example.Rmd delete mode 100644 vignettes/analysis_normal.Rmd diff --git a/DESCRIPTION b/DESCRIPTION index dabb0f8..8f8f8c3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -45,7 +45,7 @@ Suggests: spelling, testthat (>= 3.0.0) VignetteBuilder: - knitr + quarto Config/testthat/edition: 3 Encoding: UTF-8 LazyData: true diff --git a/vignettes/.gitignore b/vignettes/.gitignore index cc5d5f5..5d528d8 100644 --- a/vignettes/.gitignore +++ b/vignettes/.gitignore @@ -1,4 +1,5 @@ *.html *.R *_cache/* -*_files/* \ No newline at end of file +*_files/* +*archive \ No newline at end of file diff --git a/vignettes/Simulation_Example.Rmd b/vignettes/Simulation_Example.Rmd deleted file mode 100644 index 4b36538..0000000 --- a/vignettes/Simulation_Example.Rmd +++ /dev/null @@ -1,163 +0,0 @@ ---- -title: "Simulation Example of Bayesian MCPMod for Continuous Data" -output: rmarkdown::html_vignette -number_sections: true -vignette: > - %\VignetteIndexEntry{Simulation Example of Bayesian MCPMod for Continuous Data} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - -```{r setup} -library(BayesianMCPMod) -library(clinDR) -library(dplyr) - -set.seed(7015) -``` - -# Background and data - -In this vignette, we will show the use of the Bayesian MCPMod package for trial planning for continuous distributed data. -As in [the analysis example vignette](analysis_normal.html), we focus on the indication MDD and make use of historical data that is included in the clinDR package. -More specifically, trial results for BRINTELLIX will be utilized to establish an informative prior for the control group. - -# Calculation of a MAP prior -In a first step, a meta analytic predictive prior will be calculated using historical data from 5 trials with main endpoint Change from baseline in MADRS score after 8 weeks. -Please note that only information from the control group will be integrated leading to an informative mixture prior for the control group, while for the active groups a non-informative prior will be specified. - -```{r Historical Data} -data("metaData") -testdata <- as.data.frame(metaData) -dataset <- filter(testdata, bname == "BRINTELLIX") -histcontrol <- filter(dataset, dose == 0, primtime == 8, indication == "MAJOR DEPRESSIVE DISORDER") - -hist_data <- data.frame( - trial = histcontrol$nctno, - est = histcontrol$rslt, - se = histcontrol$se, - sd = histcontrol$sd, - n = histcontrol$sampsize) - -sd_tot <- with(hist_data, sum(sd * n) / sum(n)) -``` -We will make use of the same getPriorList() function as in the [analysis example vignette](analysis_normal.html) to create a MAP prior. -```{r Setting Prior without execution, eval = FALSE} -dose_levels <- c(0, 2.5, 5, 10, 20) - -prior_list <- getPriorList( - hist_data = hist_data, - dose_levels = dose_levels, - robust_weight = 0.3) -``` -```{r Setting Prior, echo = FALSE} -dose_levels <- c(0, 2.5, 5, 10, 20) - -prior_list <- list( - Ctr = RBesT::mixnorm( - comp1 = c(w = 0.446213, m = -12.774661, s = 1.393130), - comp1 = c(w = 0.253787, m = 3.148116, s = 3.148116), - robust = c(w = 0.3, m = 9.425139, s = 9.425139), - sigma = sd_tot), - DG_1 = RBesT::mixnorm( - comp1 = c(w = 1, m = -12.816875, n = 1), - sigma = sd_tot, - param = "mn"), - DG_2 = RBesT::mixnorm( - comp1 = c(w = 1, m = -12.816875, n = 1), - sigma = sd_tot, - param = "mn"), - DG_3 = RBesT::mixnorm( - comp1 = c(w = 1, m = -12.816875, n = 1), - sigma = sd_tot, - param = "mn"), - DG_4 = RBesT::mixnorm( - comp1 = c(w = 1, m = -12.816875, n = 1), - sigma = sd_tot, - param = "mn") -) -``` - -# Specification of new trial design - -For the hypothetical new trial, we plan with 4 active dose levels \eqn{2.5, 5, 10, 20} and we specify a broad set of potential dose-response relationships, including a linear, an exponential, an emax and 2 sigEMax models. -Furthermore, we assume a maximum effect of -3 on top of control (i.e. assuming that active treatment can reduce the MADRS score after 8 weeks by up to 15.8) and plan a trial with 80 patients for all active groups and 60 patients for control. -```{r} -exp <- DoseFinding::guesst( - d = 5, - p = c(0.2), - model = "exponential", - Maxd = max(dose_levels)) - -emax <- DoseFinding::guesst( - d = 2.5, - p = c(0.9), - model = "emax") - -sigemax <- DoseFinding::guesst( - d = c(2.5, 5), - p = c(0.1, 0.6), - model = "sigEmax") - -sigemax2 <- DoseFinding::guesst( - d = c(2, 4), - p = c(0.3, 0.8), - model = "sigEmax") - -mods <- DoseFinding::Mods( - linear = NULL, - emax = emax, - exponential = exp, - sigEmax = rbind(sigemax, sigemax2), - doses = dose_levels, - maxEff = -3, - placEff = -12.8) - -n_patients <- c(60, 80, 80, 80, 80) -``` - -# Calculation of success probabilities - -To calculate success probabilities for the different assumed dose-response models and the specified trial design we will apply the assessDesign function. -For illustration purposes, the number of simulated trial results is reduced to 100 in this example. -```{r} -success_probabilities <- assessDesign( - n_patients = n_patients, - mods = mods, - prior_list = prior_list, - sd = sd_tot, - n_sim = 100) # speed up example run-time - -success_probabilities -``` -As an alternative, we will evaluate a design with the same overall sample size but allocating more patients on the highest dose group and control. -```{r} -success_probabilities_uneq <- assessDesign( - n_patients = c(80, 60, 60, 60, 120), - mods = mods, - prior_list = prior_list, - sd = sd_tot, - n_sim = 100) # speed up example run-time -success_probabilities_uneq -``` -For this specific trial setting the adapted allocation ratio leads to increased success probabilities under all assumed dose response relationships. - -Instead of specifying the assumed effects via the models it is also possible to directly specify the effects for the individual dose levels via the dr_means input. -This allows e.g. also the simulation of scenarios with a prior-data conflict. -```{r} -success_probabilities <- assessDesign( - n_patients = c(60, 80, 80, 80, 80), - mods = mods, - prior_list = prior_list, - sd = sd_tot, - dr_means = c(-12, -14, -15, -16, -17), - n_sim = 100) # speed up example run-time -success_probabilities -``` \ No newline at end of file diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd deleted file mode 100644 index 3619620..0000000 --- a/vignettes/analysis_normal.Rmd +++ /dev/null @@ -1,279 +0,0 @@ ---- -title: "Analysis Example of Bayesian MCPMod for Continuous Data" -output: html_document -number_sections: true ---- - -```{r, include = FALSE} -knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" -) -``` - -```{r setup} -library(BayesianMCPMod) -library(clinDR) -library(dplyr) - -set.seed(7015) -``` - -# Background and data - -In this vignette we will show the use of the Bayesian MCPMod package for continuous distributed data. -The focus lies on the utilization of an informative prior and the Bayesian MCPMod evaluation of a single trial. -We will use data that is included in the clinDR package. -More specifically, trial results of BRINTELLIX will be used to illustrate the specification of an informative prior and the usage of such a prior for the Bayesian evaluation of a (hypothetical) new trial. -BRINTELLIX is a medication used to treat major depressive disorder. -Various clinical trials with different dose groups, including control groups, were conducted. - -# Calculation of a MAP prior - -In a first step, a meta analytic prior will be calculated using historical data from 4 trials with main endpoint Change from baseline in MADRS score after 8 weeks. -Please note that only information from the control group will be integrated leading to an informative mixture prior for the control group, while for the active groups a non-informative prior will be specified. -```{r Historical Data for Control Arm} -data("metaData") -dataset <- filter(as.data.frame(metaData), bname == "BRINTELLIX") -histcontrol <- filter( - dataset, - dose == 0, - primtime == 8, - indication == "MAJOR DEPRESSIVE DISORDER", - protid != 5) - -hist_data <- data.frame( - trial = histcontrol$nctno, - est = histcontrol$rslt, - se = histcontrol$se, - sd = histcontrol$sd, - n = histcontrol$sampsize) -``` -Here, we suggest a function to construct a list of prior distributions for the different dose groups. -This function is adapted to the needs of this example. -Other applications may need a different way to construct prior distributions. -```{r Defining MAP prior function} -getPriorList <- function ( - - hist_data, - dose_levels, - dose_names = NULL, - robust_weight = 0.5 - -) { - - sd_tot <- with(hist_data, sum(sd * n) / sum(n)) - - gmap <- RBesT::gMAP( - formula = cbind(est, se) ~ 1 | trial, - weights = hist_data$n, - data = hist_data, - family = gaussian, - beta.prior = cbind(0, 100 * sd_tot), - tau.dist = "HalfNormal", - tau.prior = cbind(0, sd_tot / 4)) - - prior_ctr <- RBesT::automixfit(gmap) - - if (!is.null(robust_weight)) { - - prior_ctr <- suppressMessages(RBesT::robustify( - priormix = prior_ctr, - weight = robust_weight, - sigma = sd_tot)) - - } - - prior_trt <- RBesT::mixnorm( - comp1 = c(w = 1, m = summary(prior_ctr)[1], n = 1), - sigma = sd_tot, - param = "mn") - - prior_list <- c(list(prior_ctr), - rep(x = list(prior_trt), - times = length(dose_levels[-1]))) - - if (is.null(dose_names)) { - - dose_names <- c("Ctr", paste0("DG_", seq_along(dose_levels[-1]))) - - } - - names(prior_list) <- dose_names - - return (prior_list) - -} -``` -With the dose levels to be investigated, the prior distribution can be constructed. -```{r Getting the MAP prior} -dose_levels <- c(0, 2.5, 5, 10) - -prior_list <- getPriorList( - hist_data = hist_data, - dose_levels = dose_levels, - robust_weight = 0.3) - -BayesianMCPMod::getESS(prior_list) -``` - -# Specifications for the new trial - -To be able to apply the Bayesian MCPMod approach, candidate models need to be specified using functions from the R package DoseFinding. -Since there are only 3 active dose levels we will limit the set of candidate models to a linear, an exponential, and an emax model. -Note that the linear candidate model does not require a guesstimate. -```{r Pre-Specification of candidate models} -exp_guesst <- DoseFinding::guesst( - d = 5, - p = c(0.2), - model = "exponential", - Maxd = max(dose_levels)) - -emax_guesst <- DoseFinding::guesst( - d = 2.5, - p = c(0.9), - model = "emax") - -mods <- DoseFinding::Mods( - linear = NULL, - emax = emax_guesst, - exponential = exp_guesst, - doses = dose_levels, - maxEff = -1, - placEff = -12.8) -``` -We will use the trial with ct.gov number NCT00735709 as exemplary new trial. -```{r new trial} -new_trial <- filter( - dataset, - primtime == 8, - indication == "MAJOR DEPRESSIVE DISORDER", - protid == 5) - -n_patients <- c(128, 124, 129, 122) -``` -# Combination of prior information and trial results -As outlined in Fleischer et al. [Bayesian MCPMod. Pharmaceutical Statistics. 2022; 21(3): 654-670.], in a first step the posterior is calculated combining the prior information with the estimated results of the new trial. Via the summary function it is possible to print out the summary information of the posterior distributions. -```{r Trial results} -posterior <- getPosterior( - prior = prior_list, - mu_hat = new_trial$rslt, - se_hat = new_trial$se, - calc_ess = TRUE) - -summary(posterior) -``` -# Execution of Bayesian MCPMod Test step - -For the execution of the testing step of Bayesian MCPMod a critical value on the probability scale will be determined for a given alpha level. -This critical value is calculated using the re-estimated contrasts for the frequentist MCPMod to ensure that, when using non-informative priors, the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled by the specified alpha level. - -A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see Fleischer et al. 2022 for more details). -```{r Preparation of input for Bayesian MCPMod Test step} -crit_pval <- getCritProb( - mods = mods, - dose_levels = dose_levels, - se_new_trial = new_trial$se, - alpha_crit_val = 0.05) - -contr_mat <- getContr( - mods = mods, - dose_levels = dose_levels, - sd_posterior = summary(posterior)[, 2]) -``` -Please note that there are different ways to derive the contrasts. -The following code shows the implementation of some of these ways but it is not executed and the contrast specification above is used. -```{r , eval = FALSE} -# i) the frequentist contrast -contr_mat_prior <- getContr( - mods = mods, - dose_levels = dose_levels, - dose_weights = n_patients, - prior_list = prior_list) -# ii) re-estimated frequentist contrasts -contr_mat_prior <- getContr( - mods = mods, - dose_levels = dose_levels, - se_new_trial = new_trial$se) -# iii) Bayesian approach using number of patients for new trial and prior distribution -contr_mat_prior <- getContr( - mods = mods, - dose_levels = dose_levels, - dose_weights = n_patients, - prior_list = prior_list) -``` -The Bayesian MCP testing step is then executed based on the posterior information, the provided contrasts and the multiplicity adjusted critical value. -```{r Execution of Bayesian MCPMod Test step} -BMCP_result <- performBayesianMCP( - posterior_list = posterior, - contr = contr_mat, - crit_prob_adj = crit_pval) - -BMCP_result -``` -The testing step is significant indicating a non-flat dose-response shape. -In detail, all 3 models are significant and the p-value for the emax model indicates deviation from the null hypothesis the most. - -# Model fitting and visualization of results - -In the model fitting step the posterior distribution is used as basis. -Both simplified and full fitting are performed. -For the simplified fit, the multivariate normal distribution of the control group is approximated and reduced by a one-dimensional normal distribution. -The actual fit (on this approximated posterior distribution) is then performed using generalized least squares criterion. -In contrast, for the full fit, the non-linear optimization problem is addressed via the Nelder Mead algorithm. - -The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. -For the considered case, the simplified and the full fit are very similar. -```{r Model fitting} -# Option a) Simplified approach by using approximated posterior distribution -fit_simple <- getModelFits( - models = names(mods), - dose_levels = dose_levels, - posterior = posterior, - simple = TRUE) - -# Option b) Making use of the complete posterior distribution -fit <- getModelFits( - models = names(mods), - dose_levels = dose_levels, - posterior = posterior, - simple = FALSE) -``` -Via the predict() function, one can also receive estimates for dose levels that were not included in the trial. -```{r Predict} -predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) -``` -It is possible to plot the fitted dose response models and an AIC based average model (black lines). -```{r Plot simple vs fit} -plot(fit_simple) -plot(fit) -``` -To assess the uncertainty, one can additionally visualize credible bands (orange shaded areas, default levels are 50% and 95%). -These credible bands are calculated with a bootstrap method as follows: -Samples from the posterior distribution are drawn and for every sample the simplified fitting step and a prediction is performed. -These predictions are then used to identify and visualize the specified quantiles. -```{r Plot with bootstrap} -plot(fit, cr_bands = TRUE) -``` -The bootstrap based quantiles can also be directly calculated via the getBootstrapQuantiles() function. -For this example, only 6 quantiles are bootstrapped for each model fit. -```{r Bootstrap} -getBootstrapQuantiles( - model_fits = fit, - quantiles = c(0.025, 0.5, 0.975), - doses = c(0, 2.5, 4, 5, 7, 10), - n_samples = 6) -``` -Technical note: The median quantile of the bootstrap based procedure is not necessary similar to the main model fit, as they are derived via different procedures. -The main fit, i.e. the black lines in the plot, show the best fit of a certain model based on minimizing the residuals for the posterior distribution, while the bootstrap based 50% quantile shows the median fit of the random sampling and fitting procedure. - -# Additional note -It is also possible to perform the testing and modeling step in a combined fashion via the performBayesianMCPMod() function. -This code serves merely as an example and is not run in this vignette. -```{r, eval = FALSE} -performBayesianMCPMod( - posterior_list = posterior, - contr = contr_mat, - crit_prob_adj = crit_pval, - simple = FALSE) -``` \ No newline at end of file diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index f231ae8..cba8168 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -96,24 +96,16 @@ logistic_guesst <- DoseFinding::guesst( model = "logistic", d = c(5, 10), p = c(0.1, 0.85) ) -# beta_guesst <- DoseFinding::guesst( -# model = "betaMod", -# d = 0.4, p = 0.8, dMax = 0.8, Maxd = 1, scal = 1.2 -# ) -quadratic_guesst <- DoseFinding::guesst( - model = "quadratic", - d = 5, p = 0.9 -) mods <- DoseFinding::Mods( linear = NULL, exponential = exp_guesst, emax = emax_guesst, sigEmax = sigEmax_guesst, - # linlog = NULL, logistic = logistic_guesst, - # beta = beta_guesst, - quadratic = quadratic_guesst, + linInt = c(0.5, 1, 1), + betaMod = c(1, 1), + quadratic = -0.1, doses = dose_levels, maxEff = -1, placEff = -12.8 From 4a6fbab81c826608753a2a7928bc94de730e216b Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sat, 2 Mar 2024 14:46:59 +0800 Subject: [PATCH 11/49] display treatment effects for dose groups --- vignettes/analysis_normal.qmd | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index cba8168..17b29bc 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -52,10 +52,9 @@ Non-informative priors, often chosen for their neutrality, offer a baseline from which the trial data can speak more prominently. This approach is particularly useful in early-stage research or when historical data is limited or not directly applicable. -[TODO: make a uniform prior from -100 to 100]{.aside} +[TODO: make a prior non-informative instead of weakly informative (i.e., uniform from -100 to 100)]{.aside} ```{r Non-Informative Prior Specification} -# Specifying non-informative priors for all dose groups dose_levels <- c(0, 2.5, 5, 10) prior_list <- lapply(dose_levels, function(dose_group) { @@ -114,6 +113,12 @@ mods <- DoseFinding::Mods( plot(mods) ``` +We can see the assumed treatment effects for the specified dose groups below: + +```{r} +knitr::kable(DoseFinding::getResp(mods, doses = dose_levels)) +``` + We will use the trial with ct.gov number NCT00735709 as exemplary new trial [@nct00735709_2024a]. This dataset comes from the `{clinDR}` package. From d94f8d53828c1265418ce9c09648f5afc43b2c23 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sat, 2 Mar 2024 14:49:44 +0800 Subject: [PATCH 12/49] formatting for quarto --- vignettes/analysis_normal.qmd | 42 +++++++++++++++++++---------------- 1 file changed, 23 insertions(+), 19 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 17b29bc..7f9753c 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -16,7 +16,7 @@ vignette: > %\VignetteEncoding{UTF-8} --- -```{r setup} +```{r} #| message: false #| include: false @@ -54,7 +54,7 @@ useful in early-stage research or when historical data is limited or not directl [TODO: make a prior non-informative instead of weakly informative (i.e., uniform from -100 to 100)]{.aside} -```{r Non-Informative Prior Specification} +```{r} dose_levels <- c(0, 2.5, 5, 10) prior_list <- lapply(dose_levels, function(dose_group) { @@ -78,7 +78,7 @@ Note that the linear candidate model does not require a guesstimate. [TODO: need to implement linlog shape in getOptParams()]{.aside} -```{r Pre-Specification of candidate models} +```{r} exp_guesst <- DoseFinding::guesst( model = "exponential", d = 5, p = 0.2, Maxd = max(dose_levels) @@ -122,7 +122,7 @@ knitr::kable(DoseFinding::getResp(mods, doses = dose_levels)) We will use the trial with ct.gov number NCT00735709 as exemplary new trial [@nct00735709_2024a]. This dataset comes from the `{clinDR}` package. -```{r new trial} +```{r} data("metaData") dataset <- dplyr::filter(tibble::tibble(metaData), bname == "BRINTELLIX") @@ -144,7 +144,7 @@ combining the prior information with the estimated results of the new trial. Via the summary function it is possible to print out the summary information of the posterior distributions. -```{r Trial results} +```{r} posterior <- BayesianMCPMod::getPosterior( prior_list = prior_list, mu_hat = new_trial$rslt, @@ -167,7 +167,7 @@ MCPMod is controlled by the specified alpha level. A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see [@fleischer_2022] for more details). -```{r Preparation of input for Bayesian MCPMod Test step} +```{r} crit_pval <- BayesianMCPMod::getCritProb( mods = mods, dose_levels = dose_levels, @@ -186,7 +186,9 @@ Please note that there are different ways to derive the contrasts. The following code shows the implementation of some of these ways but it is not executed and the contrast specification above is used. -```{r , eval = FALSE} +```{r} +#| eval: false + # i) the frequentist contrast contr_mat_prior <- BayesianMCPMod::getContr( mods = mods, @@ -209,7 +211,7 @@ contr_mat_prior <- BayesianMCPMod::getContr( The Bayesian MCP testing step is then executed based on the posterior information, the provided contrasts and the multiplicity adjusted critical value. -```{r Execution of Bayesian MCPMod Test step} +```{r} BMCP_result <- BayesianMCPMod::performBayesianMCP( posterior_list = posterior, contr = contr_mat, @@ -241,7 +243,7 @@ For the considered case, the simplified and the full fit are very similar. [TODO: Debug error in getModelFits: 'Error in is.nloptr(ret) : at least one element in x0 > ub']{.aside} -```{r Model fitting} +```{r} # Option a) Simplified approach by using approximated posterior distribution fit_simple <- BayesianMCPMod::getModelFits( models = names(mods), @@ -261,7 +263,7 @@ fit <- fit_simple Via the `stats::predict()` function, one can also receive estimates for dose levels that were not included in the trial. -```{r Predict} +```{r} stats::predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) stats::predict(fit_simple, doses = c(0, 2.5, 4, 5, 7, 10)) ``` @@ -269,7 +271,7 @@ stats::predict(fit_simple, doses = c(0, 2.5, 4, 5, 7, 10)) It is possible to plot the fitted dose response models and an AIC based average model (black lines). -```{r Plot simple vs fit} +```{r} #| cache: true plot(fit_simple) plot(fit) @@ -285,7 +287,7 @@ simplified fitting step and a prediction is performed. - These predictions are then used to identify and visualize the specified quantiles. -```{r Plot with bootstrap} +```{r} #| cache: true plot(fit, cr_bands = TRUE) ``` @@ -295,7 +297,7 @@ The bootstrap based quantiles can also be directly calculated via the For this example, only 6 quantiles are bootstrapped for each model fit. -```{r Bootstrap} +```{r} BayesianMCPMod::getBootstrapQuantiles( model_fits = fit, quantiles = c(0.025, 0.5, 0.975), @@ -319,10 +321,12 @@ This code serves merely as an example and is not run in this vignette. [TODO: debug error, see above about nloptr]{.aside} -```{r, eval = FALSE} -# BayesianMCPMod::performBayesianMCPMod( -# posterior_list = posterior, -# contr = contr_mat, -# crit_prob_adj = crit_pval, -# simple = FALSE) +```{r} +#| eval:false + +BayesianMCPMod::performBayesianMCPMod( + posterior_list = posterior, + contr = contr_mat, + crit_prob_adj = crit_pval, + simple = FALSE) ``` From 974aee4da8a66d68abcbe6e3b639d681921b6ccf Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sat, 2 Mar 2024 15:29:12 +0800 Subject: [PATCH 13/49] use display_params_table custom function for bmcp results --- vignettes/analysis_normal.qmd | 48 +++++++++++++++++++++++++++++++++-- 1 file changed, 46 insertions(+), 2 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 7f9753c..74de108 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -15,7 +15,51 @@ vignette: > %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} --- - + +```{r} +#| include: false + +#' Display Parameters Table +#' +#' This function generates a markdown table displaying the names and values of parameters +#' from a named list. +#' +#' @param named_list A named list where each name represents a parameter name and the list +#' element represents the parameter value. Date values in the list are automatically +#' converted to character strings for display purposes. +#' +#' @return Prints a markdown table with two columns: "Parameter Name" and "Parameter Values". +#' The function does not return a value but displays the table directly to the output. +#' +#' @importFrom knitr kable +#' @examples +#' params <- list("Start Date" = as.Date("2020-01-01"), +#' "End Date" = as.Date("2020-12-31"), +#' "Threshold" = 10) +#' display_params_table(params) +#' +#' @export +display_params_table <- function(named_list) { + display_table <- data.frame() + + for (i in 1:length(named_list)) { + # dates will display as numeric by default, so convert to char first + if (class(named_list[[i]]) == "Date") { + named_list[[i]] = as.character(named_list[[i]]) + } + + display_table <- rbind(display_table, data.frame(I(list(named_list[[i]])))) + } + + class(display_table[[1]]) <- "list" + + knitr::kable( + cbind(names(named_list), display_table), + col.names = c("Parameter Name", "Parameter Values") + ) +} +``` + ```{r} #| message: false #| include: false @@ -217,7 +261,7 @@ BMCP_result <- BayesianMCPMod::performBayesianMCP( contr = contr_mat, crit_prob_adj = crit_pval) -BMCP_result +display_params_table(BMCP_result[1,]) ``` The testing step is significant indicating a non-flat dose-response shape. From dbf7b9f4e45651c1f70d669a60f1c8e2c022e12a Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sat, 2 Mar 2024 16:20:50 +0800 Subject: [PATCH 14/49] add display for fit and bootstrap --- vignettes/analysis_normal.qmd | 83 ++++++++++++++++++++++++----------- 1 file changed, 57 insertions(+), 26 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 74de108..caa129e 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -41,18 +41,18 @@ vignette: > #' @export display_params_table <- function(named_list) { display_table <- data.frame() - + for (i in 1:length(named_list)) { # dates will display as numeric by default, so convert to char first if (class(named_list[[i]]) == "Date") { named_list[[i]] = as.character(named_list[[i]]) } - + display_table <- rbind(display_table, data.frame(I(list(named_list[[i]])))) } - + class(display_table[[1]]) <- "list" - + knitr::kable( cbind(names(named_list), display_table), col.names = c("Parameter Name", "Parameter Values") @@ -69,6 +69,7 @@ library(RBesT) library(clinDR) library(dplyr) library(tibble) +library(reactable) set.seed(7015) ``` @@ -283,41 +284,33 @@ non-linear optimization problem is addressed via the Nelder-Mead algorithm The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. -For the considered case, the simplified and the full fit are very similar. - -[TODO: Debug error in getModelFits: 'Error in is.nloptr(ret) : at least one element in x0 > ub']{.aside} +For the considered case, the simplified and the full fit are very similar, so we'll +just present the full fit. ```{r} -# Option a) Simplified approach by using approximated posterior distribution -fit_simple <- BayesianMCPMod::getModelFits( - models = names(mods), +# linInt, betaMod, quadratic are not yet supported +supported_models <- names(mods)[!names(mods) %in% c("linInt", "betaMod", "quadratic")] + +# If simple = TRUE, uses approx posterior +# Here we use complete posterior distribution +fit <- BayesianMCPMod::getModelFits( + models = supported_models, dose_levels = dose_levels, posterior = posterior, - simple = TRUE) - -# Option b) Making use of the complete posterior distribution -# fit <- BayesianMCPMod::getModelFits( -# models = names(mods), -# dose_levels = dose_levels, -# posterior = posterior, -# simple = FALSE) -fit <- fit_simple + simple = FALSE) ``` Via the `stats::predict()` function, one can also receive estimates for dose levels that were not included in the trial. ```{r} -stats::predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) -stats::predict(fit_simple, doses = c(0, 2.5, 4, 5, 7, 10)) +display_params_table(stats::predict(fit, doses = c(0, 2.5, 4, 5, 7, 10))) ``` It is possible to plot the fitted dose response models and an AIC based average model (black lines). ```{r} -#| cache: true -plot(fit_simple) plot(fit) ``` @@ -342,11 +335,49 @@ The bootstrap based quantiles can also be directly calculated via the For this example, only 6 quantiles are bootstrapped for each model fit. ```{r} -BayesianMCPMod::getBootstrapQuantiles( +bootstrap_quantiles <- BayesianMCPMod::getBootstrapQuantiles( model_fits = fit, quantiles = c(0.025, 0.5, 0.975), doses = c(0, 2.5, 4, 5, 7, 10), - n_samples = 6) + n_samples = 6 +) +``` + +```{r} +#| code-fold: true +reactable::reactable( + data = bootstrap_quantiles, + groupBy = "models", + columns = list( + doses = colDef( + aggregate = "count", + format = list( + aggregated = colFormat(suffix = " doses") + ) + ), + "2.5%" = colDef( + aggregate = "mean", + format = list( + aggregated = colFormat(prefix = "mean = ", digits = 2), + cell = colFormat(digits = 4) + ) + ), + "50%" = colDef( + aggregate = "mean", + format = list( + aggregated = colFormat(prefix = "mean = ", digits = 2), + cell = colFormat(digits = 4) + ) + ), + "97.5%" = colDef( + aggregate = "mean", + format = list( + aggregated = colFormat(prefix = "mean = ", digits = 2), + cell = colFormat(digits = 4) + ) + ) + ) +) ``` Technical note: The median quantile of the bootstrap based procedure is not @@ -366,7 +397,7 @@ This code serves merely as an example and is not run in this vignette. [TODO: debug error, see above about nloptr]{.aside} ```{r} -#| eval:false +#| eval: false BayesianMCPMod::performBayesianMCPMod( posterior_list = posterior, From ba1077d5f5db99b377b1129b6ccf9b7aafff64b1 Mon Sep 17 00:00:00 2001 From: sebastianbossert Date: Fri, 22 Mar 2024 10:34:17 +0000 Subject: [PATCH 15/49] Addition of betaMod function --- R/modelling.R | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/R/modelling.R b/R/modelling.R index 2ba5933..a7d94b6 100644 --- a/R/modelling.R +++ b/R/modelling.R @@ -147,7 +147,14 @@ getModelFitOpt <- function ( lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.01 * max(dose_levels)) ub <- c(Inf, Inf, 1.5 * max(dose_levels), 0.5 * max(dose_levels)) expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] / (1 + exp((theta[3] - dose_levels) / theta[4]))))^2 / (post_combs$vars[i, ])))}, - { + "beta" = { + lb <- c(-Inf, -Inf, 0.05, 0.05) + ub <- c(Inf, Inf, 4, 4) + scal <- attr(mod,"scal") + #Dummy code we need the information from attr(mod,"scal") which is by default 1.2*max(dose_levels) + #if(scal=NULL)(scal<-1.2*max(dose_levels)) + expr_i <- quote(sum((post_combs$means[i, ]-(theta[1]+theta[2]*(((theta[3]+theta[4])^(theta[3]+theta[4]))/((theta[3]^theta[3])*(theta[4]^theta[4])))*(dose_levels/scal)^(theta[3])*((1-dose_levels/scal)^(theta[4]) )))))} + { stop ("error: model shape not yet implemented")}) simple_fit <- getModelFitSimple( From f5fd2734b1afe8f2c73079981bfc8c8cc345c662 Mon Sep 17 00:00:00 2001 From: sebastianbossert Date: Fri, 22 Mar 2024 10:37:59 +0000 Subject: [PATCH 16/49] Name betaMod updated --- R/modelling.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/modelling.R b/R/modelling.R index a7d94b6..3920697 100644 --- a/R/modelling.R +++ b/R/modelling.R @@ -147,7 +147,7 @@ getModelFitOpt <- function ( lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.01 * max(dose_levels)) ub <- c(Inf, Inf, 1.5 * max(dose_levels), 0.5 * max(dose_levels)) expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] / (1 + exp((theta[3] - dose_levels) / theta[4]))))^2 / (post_combs$vars[i, ])))}, - "beta" = { + "betaMod" = { lb <- c(-Inf, -Inf, 0.05, 0.05) ub <- c(Inf, Inf, 4, 4) scal <- attr(mod,"scal") From f67a5fa5846d5f584771f7148b402d8136d18a4d Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Mon, 25 Mar 2024 13:47:17 +0800 Subject: [PATCH 17/49] bracket bug --- R/modelling.R | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/R/modelling.R b/R/modelling.R index 3920697..9891f95 100644 --- a/R/modelling.R +++ b/R/modelling.R @@ -67,7 +67,7 @@ getModelFits <- function ( attr(model_fits, "posterior") <- posterior class(model_fits) <- "modelFits" - + return (model_fits) } @@ -153,9 +153,8 @@ getModelFitOpt <- function ( scal <- attr(mod,"scal") #Dummy code we need the information from attr(mod,"scal") which is by default 1.2*max(dose_levels) #if(scal=NULL)(scal<-1.2*max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ]-(theta[1]+theta[2]*(((theta[3]+theta[4])^(theta[3]+theta[4]))/((theta[3]^theta[3])*(theta[4]^theta[4])))*(dose_levels/scal)^(theta[3])*((1-dose_levels/scal)^(theta[4]) )))))} - { - stop ("error: model shape not yet implemented")}) + expr_i <- quote(sum((post_combs$means[i, ]-(theta[1]+theta[2]*(((theta[3]+theta[4])^(theta[3]+theta[4]))/((theta[3]^theta[3])*(theta[4]^theta[4])))*(dose_levels/scal)^(theta[3])*((1-dose_levels/scal)^(theta[4]))))))}, + stop ("error: model shape not yet implemented")) simple_fit <- getModelFitSimple( model = model, @@ -193,7 +192,7 @@ getModelFitOpt <- function ( param_list <- getOptParams(model, dose_levels, posterior) post_combs <- getPostCombsI(posterior) - + fit <- nloptr::nloptr( eval_f = optFun, x0 = param_list$params$x0, From 8c5dc369f6cf58a38b57f540ff9721e4dd711db9 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Mon, 25 Mar 2024 15:32:43 +0800 Subject: [PATCH 18/49] updated dose response model parameterization section --- vignettes/analysis_normal.qmd | 96 ++++++++++++++++++++++++++++------- 1 file changed, 77 insertions(+), 19 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index caa129e..30f2a24 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -41,22 +41,32 @@ vignette: > #' @export display_params_table <- function(named_list) { display_table <- data.frame() - + value_names <- data.frame() for (i in 1:length(named_list)) { # dates will display as numeric by default, so convert to char first if (class(named_list[[i]]) == "Date") { named_list[[i]] = as.character(named_list[[i]]) } - - display_table <- rbind(display_table, data.frame(I(list(named_list[[i]])))) + if (!is.null(names(named_list[[i]]))) { + value_names <- rbind(value_names, paste(names(named_list[[i]]), collapse = ', ')) + } + values <- data.frame(I(list(named_list[[i]]))) + display_table <- rbind(display_table, values) } class(display_table[[1]]) <- "list" - knitr::kable( - cbind(names(named_list), display_table), - col.names = c("Parameter Name", "Parameter Values") - ) + if (nrow(value_names) == 0) { + knitr::kable( + cbind(names(named_list), display_table), + col.names = c("Name", "Value") + ) + } else { + knitr::kable( + cbind(names(named_list), value_names, display_table), + col.names = c("Name", "Value Labels", "Value") + ) + } } ``` @@ -89,16 +99,14 @@ prior for the Bayesian MCPMod evaluation of a single trial. # Non-Informative Prior Specification Rather than constructing a complex mixture prior for the control group and -differentiating active groups with detailed priors, we will specify non-informative +differentiating active groups with detailed priors, we will specify weakly-informative priors across all dose groups. This simplification aims to underscore the flexibility in prior selection and its impact on the Bayesian analysis framework. -Non-informative priors, often chosen for their neutrality, offer a baseline +Weakly-informative priors, often chosen for their neutrality, offer a baseline from which the trial data can speak more prominently. This approach is particularly useful in early-stage research or when historical data is limited or not directly applicable. -[TODO: make a prior non-informative instead of weakly informative (i.e., uniform from -100 to 100)]{.aside} - ```{r} dose_levels <- c(0, 2.5, 5, 10) @@ -114,14 +122,15 @@ names(prior_list) <- c("Ctr", paste0("DG_", dose_levels[-1])) To be able to apply the Bayesian MCPMod approach, candidate models need to be specified using functions from the R package DoseFinding. -Since there are only 3 active dose levels we will limit the set of candidate -models to a linear, an exponential, and an emax model. +In a real life scenario, we would typically only include a few candidate models, maybe 4 or 5. -Note that the linear candidate model does not require a guesstimate. +For the purpose of the exposition, we will generate a variety of models to demonstrate +different approaches to the problem. Model shapes can be parameterized using `DoseFinding::guesstimate` +as well as directly providing parameters -[TODO: figure out how to paramaterize a beta]{.aside} +Note that the linear candidate model does not require a guesstimate. -[TODO: need to implement linlog shape in getOptParams()]{.aside} +[**Note:** The LinLog model is rarely used and not currently supported by `{BayesianMCPMod}`.]{.aside} ```{r} exp_guesst <- DoseFinding::guesst( @@ -140,16 +149,59 @@ logistic_guesst <- DoseFinding::guesst( model = "logistic", d = c(5, 10), p = c(0.1, 0.85) ) +``` + +In the code above, the models are specified using `DoseFinding::guesst` function. +The `d` option usually takes a single value (a dose level), and the corresponding `p` +for the maximum effect achieved at `d`. + +In some cases, you need to provide more information. For instance, sigEmax requires a pair of `d` and `p`, and +exponential requires the specification of the maximum dose for the trial. + +[See the help files for more details by typing `?DoseFinding::guesst` in your console.]{.aside} + +The output of this function ius a parameterization of the dose-response model. For example, the +parameters for the `sigEmax_guesst` are `r sigEmax_guesst` and would correspond to the last +two parameters in the `DoseFinding::sigEmax` function, e.g., + +```{r} +DoseFinding::sigEmax( + dose = c(2.5, 5), + e0 = 0, # placebo effect + eMax = 1, # max effect + ed50 = sigEmax_guesst[["ed50"]], # dose that yields half of eMax + h = sigEmax_guesst[["h"]] # Hill parameter (steepness at ed50) +) +``` + +So essentially what's happening here is that `{DoseFinding}` is providing an abstraction +so you just have to worry about the essential parameters for each dose-response shape. +You can also specify the models directly (without using `DoseFinding::guesst`). + +For example, the betaMod model only requires `delta1` and `delta2` parameters (`scale` is assumed to be `1.2`), +and the quadratic model only requires a `delta2` parameter. + +```{r} +betaMod_params <- c(delta1 = 1, delta2 = 1) +quadratic_params <- c(delta2 = -0.1) +``` + +For a more in-depth explanation of the various dose-response model shapes and how they are +parameterized, refer to the model-specific functions in the `{DoseFinding}` package. + +Now, we can go ahead and create a `Mods` object, which will be used in the remainder +of the vignette. + +```{r} mods <- DoseFinding::Mods( linear = NULL, exponential = exp_guesst, emax = emax_guesst, sigEmax = sigEmax_guesst, logistic = logistic_guesst, - linInt = c(0.5, 1, 1), - betaMod = c(1, 1), - quadratic = -0.1, + betaMod = betaMod_params, + quadratic = quadratic_params, doses = dose_levels, maxEff = -1, placEff = -12.8 @@ -157,6 +209,12 @@ mods <- DoseFinding::Mods( plot(mods) ``` +The `mods` object also contains the model parameters, which can be helpful for +understanding how the guesstimates are translated into the parameter scale. + +```{r} +display_params_table(mods) +``` We can see the assumed treatment effects for the specified dose groups below: From e8f3ce10f043671a89d2d5e0d173db945430244c Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Wed, 27 Mar 2024 12:57:23 +0800 Subject: [PATCH 19/49] minor edits --- vignettes/analysis_normal.qmd | 44 +++++++++++++++++++++-------------- 1 file changed, 27 insertions(+), 17 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 30f2a24..a92e982 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -119,6 +119,8 @@ names(prior_list) <- c("Ctr", paste0("DG_", dose_levels[-1])) # Specifications for the new trial +## Specifying dose-response model shapes + To be able to apply the Bayesian MCPMod approach, candidate models need to be specified using functions from the R package DoseFinding. @@ -174,13 +176,15 @@ DoseFinding::sigEmax( ) ``` -So essentially what's happening here is that `{DoseFinding}` is providing an abstraction -so you just have to worry about the essential parameters for each dose-response shape. +Essentially what's happening here is that `{DoseFinding}` is providing you an abstraction +such that you do not have to worry about the essential parameters for each dose-response shape, +but just the ones that are relatable in a clinical context, such as the dose (`d`) +and the maximum effect (`p`) at `d`. -You can also specify the models directly (without using `DoseFinding::guesst`). +Of course, you can also specify the models directly on the parameter scale (without using `DoseFinding::guesst`). -For example, the betaMod model only requires `delta1` and `delta2` parameters (`scale` is assumed to be `1.2`), -and the quadratic model only requires a `delta2` parameter. +For example, you can get a betaMod model by specifying `delta1` and `delta2` +parameters (`scale` is assumed to be `1.2`), or a quadratic model with the `delta2` parameter. ```{r} betaMod_params <- c(delta1 = 1, delta2 = 1) @@ -196,12 +200,15 @@ of the vignette. ```{r} mods <- DoseFinding::Mods( linear = NULL, + # guesstimate scale exponential = exp_guesst, emax = emax_guesst, sigEmax = sigEmax_guesst, logistic = logistic_guesst, + # parameter scale betaMod = betaMod_params, quadratic = quadratic_params, + # Options for all models doses = dose_levels, maxEff = -1, placEff = -12.8 @@ -209,28 +216,30 @@ mods <- DoseFinding::Mods( plot(mods) ``` -The `mods` object also contains the model parameters, which can be helpful for -understanding how the guesstimates are translated into the parameter scale. +The `mods` object we just created above contains the full model parameters, which can be helpful for +understanding how the guesstimates are translated onto the parameter scale. ```{r} display_params_table(mods) ``` -We can see the assumed treatment effects for the specified dose groups below: +And we can see the assumed treatment effects for the specified dose groups below: ```{r} knitr::kable(DoseFinding::getResp(mods, doses = dose_levels)) ``` -We will use the trial with ct.gov number NCT00735709 as exemplary new trial -[@nct00735709_2024a]. This dataset comes from the `{clinDR}` package. +## Trial data for the example + +We will use the trial with ct.gov number NCT00735709 as our new trial data +[@nct00735709_2024a]. This dataset comes from the `{clinDR}` package. Make sure +you have it installed before running the code below. ```{r} data("metaData") -dataset <- dplyr::filter(tibble::tibble(metaData), bname == "BRINTELLIX") -new_trial <- dplyr::filter( - dataset, +trial_data <- dplyr::filter( + dplyr::filter(tibble::tibble(metaData), bname == "BRINTELLIX"), primtime == 8, indication == "MAJOR DEPRESSIVE DISORDER", protid == 5 @@ -241,11 +250,12 @@ n_patients <- c(128, 124, 129, 122) # Combination of prior information and trial results -As outlined in [@fleischer_2022], in a first step the posterior is calculated -combining the prior information with the estimated results of the new trial. +As outlined in [@fleischer_2022], in the first step the posterior is calculated +by combining the prior information with the estimated results of the new trial. -Via the summary function it is possible to print out the summary information of -the posterior distributions. +Via the summary function, it is possible to print out the summary information of +the posterior distributions. Remember that our `prior_list` object is defined above +using a weakly-informative prior. ```{r} posterior <- BayesianMCPMod::getPosterior( From c2ee05587f6ef46a5bee79efeea4f52042098a73 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Wed, 27 Mar 2024 14:50:52 +0800 Subject: [PATCH 20/49] ... --- vignettes/analysis_normal.qmd | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index a92e982..8951549 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -111,7 +111,7 @@ useful in early-stage research or when historical data is limited or not directl dose_levels <- c(0, 2.5, 5, 10) prior_list <- lapply(dose_levels, function(dose_group) { - RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200)) # weakly informative normal distribution + RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200), sigma = 10) # weakly informative normal distribution }) names(prior_list) <- c("Ctr", paste0("DG_", dose_levels[-1])) @@ -170,7 +170,7 @@ two parameters in the `DoseFinding::sigEmax` function, e.g., DoseFinding::sigEmax( dose = c(2.5, 5), e0 = 0, # placebo effect - eMax = 1, # max effect + eMax = -1, # max effect ed50 = sigEmax_guesst[["ed50"]], # dose that yields half of eMax h = sigEmax_guesst[["h"]] # Hill parameter (steepness at ed50) ) @@ -251,7 +251,7 @@ n_patients <- c(128, 124, 129, 122) # Combination of prior information and trial results As outlined in [@fleischer_2022], in the first step the posterior is calculated -by combining the prior information with the estimated results of the new trial. +by combining the prior information with the estimated results of the trial. Via the summary function, it is possible to print out the summary information of the posterior distributions. Remember that our `prior_list` object is defined above @@ -260,8 +260,9 @@ using a weakly-informative prior. ```{r} posterior <- BayesianMCPMod::getPosterior( prior_list = prior_list, - mu_hat = new_trial$rslt, - se_hat = new_trial$se + mu_hat = trial_data$rslt, + se_hat = trial_data$se, + calc_ess = TRUE ) knitr::kable(summary(posterior)) @@ -273,7 +274,7 @@ For the execution of the testing step of Bayesian MCPMod a critical value on the probability scale will be determined for a given alpha level. This critical value is calculated using the re-estimated contrasts for the -frequentist MCPMod to ensure that, when using non-informative priors, +frequentist MCPMod to ensure that, when using weakly-informative priors, the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled by the specified alpha level. @@ -284,7 +285,7 @@ posterior distribution (see [@fleischer_2022] for more details). crit_pval <- BayesianMCPMod::getCritProb( mods = mods, dose_levels = dose_levels, - se_new_trial = new_trial$se, + se_new_trial = trial_data$se, alpha_crit_val = 0.05 ) @@ -312,7 +313,7 @@ contr_mat_prior <- BayesianMCPMod::getContr( contr_mat_prior <- BayesianMCPMod::getContr( mods = mods, dose_levels = dose_levels, - se_new_trial = new_trial$se) + se_new_trial = trial_data$se) # iii) Bayesian approach using number of patients for new trial and prior distribution contr_mat_prior <- BayesianMCPMod::getContr( mods = mods, From 1af58cde9e7bc5af80fcee60328e1da25381d352 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Fri, 29 Mar 2024 17:48:54 +0800 Subject: [PATCH 21/49] edits to readability, flow of analysis_vignette; allowed getModelFits to access scal param for betaMod shape; improved display of BMCP_result --- DESCRIPTION | 2 +- R/modelling.R | 101 ++++++++++++++++------------- vignettes/analysis_normal.qmd | 117 +++++++++++++++++++++------------- 3 files changed, 130 insertions(+), 90 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8f8f8c3..28db9cd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -50,5 +50,5 @@ Config/testthat/edition: 3 Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.1 Language: en-US diff --git a/R/modelling.R b/R/modelling.R index 9891f95..11dfcbe 100644 --- a/R/modelling.R +++ b/R/modelling.R @@ -56,14 +56,14 @@ getModelFits <- function ( checkmate::check_class(posterior, "postList") checkmate::check_logical(simple) - models <- unique(gsub("\\d", "", models)) - + model_names <- unique(gsub("\\d", "", names(models))) + getModelFit <- ifelse(simple, getModelFitSimple, getModelFitOpt) - model_fits <- lapply(models, getModelFit, dose_levels, posterior) + model_fits <- lapply(model_names, getModelFit, dose_levels, posterior, list("scal" = attr(models, "scal"))) model_fits <- addModelWeights(model_fits) - names(model_fits) <- models + names(model_fits) <- model_names attr(model_fits, "posterior") <- posterior class(model_fits) <- "modelFits" @@ -77,7 +77,8 @@ getModelFitSimple <- function ( model, dose_levels, - posterior + posterior, + addArgs = NULL ) { @@ -110,7 +111,8 @@ getModelFitOpt <- function ( model, dose_levels, - posterior + posterior, + addArgs = NULL ) { @@ -118,43 +120,55 @@ getModelFitOpt <- function ( model, dose_levels, - posterior + posterior, + addArgs = NULL ) { - - switch (model, - "emax" = { - lb <- c(-Inf, -Inf, 0.001 * max(dose_levels)) - ub <- c(Inf, Inf, 1.5 * max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels) / (theta[3] + dose_levels)))^2 / (post_combs$vars[i, ])))}, - "sigEmax" = { - lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.5) - ub <- c(Inf, Inf, 1.5 * max(dose_levels), 10) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels^theta[4]) / (theta[3]^theta[4] + dose_levels^theta[4])))^2 / (post_combs$vars[i, ])))}, - "exponential" = { - lb <- c(-Inf, -Inf, 0.1 * max(dose_levels)) - ub <- c(Inf, Inf, 2 * max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * (exp(dose_levels / theta[3]) - 1)))^2 / (post_combs$vars[i, ])))}, - "quadratic" = { - lb <- NULL - ub <- c(Inf, Inf, 0) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels + theta[3] * dose_levels^2))^2 / (post_combs$vars[i, ])))}, - "linear" = { - lb <- NULL - ub <- NULL - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels))^2 / (post_combs$vars[i, ])))}, - "logistic" = { - lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.01 * max(dose_levels)) - ub <- c(Inf, Inf, 1.5 * max(dose_levels), 0.5 * max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] / (1 + exp((theta[3] - dose_levels) / theta[4]))))^2 / (post_combs$vars[i, ])))}, - "betaMod" = { - lb <- c(-Inf, -Inf, 0.05, 0.05) - ub <- c(Inf, Inf, 4, 4) - scal <- attr(mod,"scal") - #Dummy code we need the information from attr(mod,"scal") which is by default 1.2*max(dose_levels) - #if(scal=NULL)(scal<-1.2*max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ]-(theta[1]+theta[2]*(((theta[3]+theta[4])^(theta[3]+theta[4]))/((theta[3]^theta[3])*(theta[4]^theta[4])))*(dose_levels/scal)^(theta[3])*((1-dose_levels/scal)^(theta[4]))))))}, - stop ("error: model shape not yet implemented")) + switch ( + model, + "emax" = { + lb <- c(-Inf, -Inf, 0.001 * max(dose_levels)) + ub <- c(Inf, Inf, 1.5 * max(dose_levels)) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels) / (theta[3] + dose_levels)))^2 / (post_combs$vars[i, ]))) + }, + "sigEmax" = { + lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.5) + ub <- c(Inf, Inf, 1.5 * max(dose_levels), 10) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels^theta[4]) / (theta[3]^theta[4] + dose_levels^theta[4])))^2 / (post_combs$vars[i, ]))) + }, + "exponential" = { + lb <- c(-Inf, -Inf, 0.1 * max(dose_levels)) + ub <- c(Inf, Inf, 2 * max(dose_levels)) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * (exp(dose_levels / theta[3]) - 1)))^2 / (post_combs$vars[i, ]))) + }, + "quadratic" = { + lb <- NULL + ub <- c(Inf, Inf, 0) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels + theta[3] * dose_levels^2))^2 / (post_combs$vars[i, ]))) + }, + "linear" = { + lb <- NULL + ub <- NULL + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels))^2 / (post_combs$vars[i, ]))) + }, + "logistic" = { + lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.01 * max(dose_levels)) + ub <- c(Inf, Inf, 1.5 * max(dose_levels), 0.5 * max(dose_levels)) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] / (1 + exp((theta[3] - dose_levels) / theta[4]))))^2 / (post_combs$vars[i, ]))) + }, + "betaMod" = { + lb <- c(-Inf, -Inf, 0.05, 0.05) + ub <- c(Inf, Inf, 4, 4) + scal <- ifelse(is.null(addArgs), 1.2 * max(dose_levels), addArgs[["scal"]]) + expr_i <- substitute( + sum( + (post_combs$means[i, ] - (theta[1] + theta[2] * (((theta[3] + theta[4])^(theta[3] + theta[4])) / ((theta[3] ^ theta[3]) * (theta[4]^theta[4]))) * (dose_levels / scal)^(theta[3]) * ((1 - dose_levels / scal)^(theta[4])))) + ), + list(scal = scal) + ) + }, + stop(paste("error:", model, "shape not yet implemented")) + ) simple_fit <- getModelFitSimple( model = model, @@ -201,7 +215,8 @@ getModelFitOpt <- function ( opts = param_list$opts, dose_levels = dose_levels, post_combs = post_combs, - expr_i = param_list$expr_i) + expr_i = param_list$expr_i + ) names(fit$solution) <- param_list$c_names @@ -264,7 +279,7 @@ predictModelFit <- function ( model_fit$coeffs["eMax"], model_fit$coeffs["ed50"], model_fit$coeffs["delta"])}, - {stop("error")}) + {stop(paste("error:", model_fit$model, "shape not yet implemented"))}) return (pred_vals) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 8951549..bbd4446 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -12,8 +12,10 @@ format: bibliography: references.bib vignette: > %\VignetteIndexEntry{Analysis Example of Bayesian MCPMod for Continuous Data} - %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} + %\VignetteEngine{quarto::html} +editor_options: + chunk_output_type: console --- ```{r} @@ -86,17 +88,18 @@ set.seed(7015) # Background and data -In this vignette we will show the use of the Bayesian MCPMod package for -continuous distributed data. +In this vignette we will show the use of the `{BayesianMCPMod}` package for the analysis +of a phase 2 dose-finding trial. -Priors can be grounded in historical data, leveraging the `{RBesT}` package functions -to construct the informative priors. This approach allows for the synthesis of prior -knowledge with current data, enhancing the accuracy of trial evaluations. +Since we're working in the Bayesian framework, we need to specify a prior. -The focus of this vignette is more generic, however. We utilize a non-informative -prior for the Bayesian MCPMod evaluation of a single trial. +Ideally, priors are grounded in historical data. This approach allows for the synthesis of +prior knowledge with current data, enhancing the accuracy of trial evaluations. -# Non-Informative Prior Specification +The focus of this vignette is more generic, however. We utilize a weakly-informative +prior for the Bayesian MCPMod evaluation of a phase 2 trial. + +# Weakly-Informative Prior Specification Rather than constructing a complex mixture prior for the control group and differentiating active groups with detailed priors, we will specify weakly-informative @@ -106,12 +109,14 @@ flexibility in prior selection and its impact on the Bayesian analysis framework Weakly-informative priors, often chosen for their neutrality, offer a baseline from which the trial data can speak more prominently. This approach is particularly useful in early-stage research or when historical data is limited or not directly applicable. +It also allows one to add in external data when/if it becomes available. ```{r} dose_levels <- c(0, 2.5, 5, 10) prior_list <- lapply(dose_levels, function(dose_group) { - RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200), sigma = 10) # weakly informative normal distribution + # weakly informative normal distribution + RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200), sigma = 10) }) names(prior_list) <- c("Ctr", paste0("DG_", dose_levels[-1])) @@ -126,15 +131,20 @@ specified using functions from the R package DoseFinding. In a real life scenario, we would typically only include a few candidate models, maybe 4 or 5. -For the purpose of the exposition, we will generate a variety of models to demonstrate -different approaches to the problem. Model shapes can be parameterized using `DoseFinding::guesstimate` -as well as directly providing parameters +For the purpose of the exposition, we will generate a greater variety of models to demonstrate +different approaches to the problem. Model shapes can be parameterized using guesstimates +based on limited information as well as directly providing the appropriate distribution parameters. -Note that the linear candidate model does not require a guesstimate. +Note that the linear candidate model does not require parameterization. [**Note:** The LinLog model is rarely used and not currently supported by `{BayesianMCPMod}`.]{.aside} +In the code below, the models are "guesstimated" using the `DoseFinding::guesst` function. +The `d` option usually takes a single value (a dose level), and the corresponding `p` +for the maximum effect achieved at `d`. + ```{r} +# Guesstimate estimation exp_guesst <- DoseFinding::guesst( model = "exponential", d = 5, p = 0.2, Maxd = max(dose_levels) @@ -153,22 +163,19 @@ logistic_guesst <- DoseFinding::guesst( ) ``` -In the code above, the models are specified using `DoseFinding::guesst` function. -The `d` option usually takes a single value (a dose level), and the corresponding `p` -for the maximum effect achieved at `d`. - -In some cases, you need to provide more information. For instance, sigEmax requires a pair of `d` and `p`, and -exponential requires the specification of the maximum dose for the trial. +In some cases, you need to provide more information. For instance, `sigEmax` +requires a pair of `d` and `p` values, and `exponential` requires the specification of +the maximum dose for the trial (`Maxd`). -[See the help files for more details by typing `?DoseFinding::guesst` in your console.]{.aside} +See the help files for model specifications by typing `?DoseFinding::guesst` in your console. -The output of this function ius a parameterization of the dose-response model. For example, the +The output of this function is a parameterization of the dose-response model. For example, the parameters for the `sigEmax_guesst` are `r sigEmax_guesst` and would correspond to the last two parameters in the `DoseFinding::sigEmax` function, e.g., ```{r} DoseFinding::sigEmax( - dose = c(2.5, 5), + dose = c(2.5, 5), # dose levels e0 = 0, # placebo effect eMax = -1, # max effect ed50 = sigEmax_guesst[["ed50"]], # dose that yields half of eMax @@ -176,10 +183,10 @@ DoseFinding::sigEmax( ) ``` -Essentially what's happening here is that `{DoseFinding}` is providing you an abstraction -such that you do not have to worry about the essential parameters for each dose-response shape, -but just the ones that are relatable in a clinical context, such as the dose (`d`) -and the maximum effect (`p`) at `d`. +Essentially `{DoseFinding}` is providing an abstraction that allows us to use +clinically meaningful and relatable variables to specify the model shape. This makes +it much easier to go from discussions within the trial team to the implementation of +the MCPMod framework. Of course, you can also specify the models directly on the parameter scale (without using `DoseFinding::guesst`). @@ -191,8 +198,8 @@ betaMod_params <- c(delta1 = 1, delta2 = 1) quadratic_params <- c(delta2 = -0.1) ``` -For a more in-depth explanation of the various dose-response model shapes and how they are -parameterized, refer to the model-specific functions in the `{DoseFinding}` package. +Again, you can reference the `{DoseFinding}` package's help files for how to parameterize +the dose-response models. Now, we can go ahead and create a `Mods` object, which will be used in the remainder of the vignette. @@ -216,6 +223,7 @@ mods <- DoseFinding::Mods( plot(mods) ``` + The `mods` object we just created above contains the full model parameters, which can be helpful for understanding how the guesstimates are translated onto the parameter scale. @@ -231,9 +239,11 @@ knitr::kable(DoseFinding::getResp(mods, doses = dose_levels)) ## Trial data for the example -We will use the trial with ct.gov number NCT00735709 as our new trial data -[@nct00735709_2024a]. This dataset comes from the `{clinDR}` package. Make sure -you have it installed before running the code below. +We will use the trial with ct.gov number NCT00735709 as our phase 2 trial data +that we'd like to apply the Bayesian MCPMod approach to [@nct00735709_2024a]. + +This dataset comes from the `{clinDR}` package. Make sure you have it installed +before running the code below. ```{r} data("metaData") @@ -250,8 +260,8 @@ n_patients <- c(128, 124, 129, 122) # Combination of prior information and trial results -As outlined in [@fleischer_2022], in the first step the posterior is calculated -by combining the prior information with the estimated results of the trial. +In the first step of Bayesian MCPMod, the posterior is calculated by combining +the prior information with the estimated results of the trial [@fleischer_2022]. Via the summary function, it is possible to print out the summary information of the posterior distributions. Remember that our `prior_list` object is defined above @@ -268,15 +278,15 @@ posterior <- BayesianMCPMod::getPosterior( knitr::kable(summary(posterior)) ``` -# Execution of Bayesian MCPMod Test step +# Execution of the Bayesian MCPMod test step -For the execution of the testing step of Bayesian MCPMod a critical value on the -probability scale will be determined for a given alpha level. +For the execution of the testing step of Bayesian MCPMod, a critical value on the +probability scale is determined for a given alpha level. This critical value is calculated using the re-estimated contrasts for the frequentist MCPMod to ensure that, when using weakly-informative priors, -the actual error level for falsely declaring a significant trial in the Bayesian -MCPMod is controlled by the specified alpha level. +the actual error level for falsely declaring a significant trial in is controlled +by the specified alpha level. A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see [@fleischer_2022] for more details). @@ -330,18 +340,36 @@ BMCP_result <- BayesianMCPMod::performBayesianMCP( posterior_list = posterior, contr = contr_mat, crit_prob_adj = crit_pval) +``` + +Summary information -display_params_table(BMCP_result[1,]) +```{r} +display_params_table(BMCP_result[1, c(1:3)]) +``` + +Effective sample size (ESS) for each dose group + +```{r} +display_params_table(attr(BMCP_result, "ess_avg")) +``` + +Posterior probabilities for each model shape + +```{r} +display_params_table((BMCP_result[1, c(4:ncol(BMCP_result))])) ``` The testing step is significant indicating a non-flat dose-response shape. -In detail, all 3 models are significant and the p-value for the emax model -indicates deviation from the null hypothesis the most. +Further, all models are significant and the p-value for the `emax` model +indicates the greatest deviation from the null hypothesis. # Model fitting and visualization of results In the model fitting step the posterior distribution is used as basis. + Both simplified and full fitting are performed. + For the simplified fit, the multivariate normal distribution of the control group is approximated and reduced by a one-dimensional normal distribution. @@ -357,13 +385,10 @@ For the considered case, the simplified and the full fit are very similar, so we just present the full fit. ```{r} -# linInt, betaMod, quadratic are not yet supported -supported_models <- names(mods)[!names(mods) %in% c("linInt", "betaMod", "quadratic")] - # If simple = TRUE, uses approx posterior # Here we use complete posterior distribution fit <- BayesianMCPMod::getModelFits( - models = supported_models, + models = mods, dose_levels = dose_levels, posterior = posterior, simple = FALSE) From d1ea476c80c6f94538edcae69ea04e8b2b754a1f Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:12:30 +0800 Subject: [PATCH 22/49] added vignette dependencies to suggests --- DESCRIPTION | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 28db9cd..f9dcc7d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -38,14 +38,16 @@ Imports: RBesT, stats Suggests: + reactable, + tibble, + quarto, clinDR, dplyr, knitr, rmarkdown, spelling, testthat (>= 3.0.0) -VignetteBuilder: - quarto +VignetteBuilder: quarto Config/testthat/edition: 3 Encoding: UTF-8 LazyData: true From 688918ce7cbb7a9dad71876a4717f76a5da5884d Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:13:16 +0800 Subject: [PATCH 23/49] corrected spelling error for modelling --- R/BMCPMod.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/BMCPMod.R b/R/BMCPMod.R index 790b51e..7c0ac21 100644 --- a/R/BMCPMod.R +++ b/R/BMCPMod.R @@ -322,7 +322,7 @@ getCritProb <- function ( #' @title performBayesianMCPMod #' -#' @description Performs Bayesian MCP Test step and modeling in a combined fashion. See performBayesianMCP() function for MCP Test step and getModelFits() for the modelling step +#' @description Performs Bayesian MCP Test step and modeling in a combined fashion. See performBayesianMCP() function for MCP Test step and getModelFits() for the modeling step #' #' @param posterior_list An object of class 'postList' as created by getPosterior() containing information about the (mixture) posterior distribution per dose group #' @param contr An object of class 'optContr' as created by the getContr() function. It contains the contrast matrix to be used for the testing step. @@ -361,7 +361,7 @@ getCritProb <- function ( #' crit_prob_adj = critVal, #' simple = FALSE) #' -#' @return Bayesian MCP test result as well as modelling result. +#' @return Bayesian MCP test result as well as modeling result. #' #' @export performBayesianMCPMod <- function ( @@ -405,7 +405,7 @@ performBayesianMCPMod <- function ( stop ("Argument 'contr' must be of type 'optContr'") } - + b_mcp <- performBayesianMCP( posterior_list = posterior_list, contr = contr, @@ -414,7 +414,7 @@ performBayesianMCPMod <- function ( fits_list <- lapply(seq_along(posterior_list), function (i) { if (b_mcp[i, 1]) { - + sign_models <- b_mcp[i, -c(1, 2)] > attr(b_mcp, "crit_prob_adj") model_fits <- getModelFits( From b88194a808dfb0234503558d3965500d577701d0 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:13:29 +0800 Subject: [PATCH 24/49] ... --- R/modeling.R | 326 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 326 insertions(+) create mode 100644 R/modeling.R diff --git a/R/modeling.R b/R/modeling.R new file mode 100644 index 0000000..28c90a8 --- /dev/null +++ b/R/modeling.R @@ -0,0 +1,326 @@ +#' @title getModelFits +#' +#' @description Fits dose-response curves for the specified dose-response models, based on the posterior distributions. +#' For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions. +#' For the default case, the Nelder-Mead algorithm is used. +#' In detail, for both approaches the mean vector \eqn{\theta^{Y}} and the covariance \eqn{\Sigma} of the (mixture) posterior distributions and the corresponding posterior weights \eqn{\tilde{\omega}_{l}} for \eqn{l \in {1,...,L}} are used as basis +#' For the full fit a GLS estimator is used to minimize the following expression for the respective dose-response models \eqn{m} +#' \deqn{ \hat{\theta}_{m}=argmin_{\theta_{m}} \sum_{l=1}^{L} \tilde{\omega}_{l}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))'\Sigma_{l}^{-1}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))} +#' Therefore the function nloptr of the nloptr package is utilized. +#' In the simplified case \eqn{L=1}, as the dimension of the posterior is reduced to 1 first. +#' The generalized AIC values are calculated via the formula +#' \deqn{gAIC_{m} = \sum_{l=1}^{L} \tilde{\omega}_{l} \sum_{i=0}^{K} \frac{1}{\Sigma_{l_{i,i}}} (\theta_{l_i}^Y - f(dose_{i},\hat{\theta}_{m}))^2 + 2p } +#' where \eqn{p} denotes the number of estimated parameters and \eqn{K} the number of active dose levels. +#' Here as well for the simplified case the formula reduces to one summand as \eqn{L=1}. +#' Corresponding gAIC based weights for model \eqn{M} are calculated as outlined in Schorning et al. (2016) +#' \deqn{ +#' \Omega_I (M) = \frac{\exp(-0.5 gAIC_{M})}{\sum_{m=1}^{Q} \exp(-0.5 gAIC_{m})} +#' } +#' where \eqn{Q} denotes the number of models included in the averaging procedure. +#' @references Schorning K, Bornkamp B, Bretz F, Dette H. 2016. Model selection versus model averaging in dose finding studies. Stat Med; 35; 4021-4040. +#' @param models List of model names for which a fit will be performed. +#' @param dose_levels A vector containing the different dosage levels. +#' @param posterior A getPosterior object, containing the (multivariate) posterior distribution per dosage level. +#' @param simple Boolean variable, defining whether simplified fit will be applied. Default FALSE. +#' @examples +#' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), +#' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), +#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , +#' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , +#' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) +#' models <- c("emax", "exponential", "sigEmax", "linear") +#' dose_levels <- c(0, 1, 2, 4, 8) +#' +#' fit <- getModelFits(models = models, +#' posterior = posterior_list, +#' dose_levels = dose_levels) +#' fit_simple <- getModelFits(models = models, +#' posterior = posterior_list, +#' dose_levels = dose_levels, +#' simple = TRUE) +#' +#' @return An object of class modelFits. A list containing information about the fitted model coefficients, the prediction per dose group as well as maximum effect and generalized AIC (and corresponding weight) per model. +#' +#' @export +getModelFits <- function ( + + models, + dose_levels, + posterior, + simple = FALSE + +) { + if (inherits(models, "character")) { + models <- stats::setNames(as.list(models), models) + } + checkmate::check_list(models, any.missing = FALSE) + checkmate::check_double(dose_levels, lower = 0, any.missing = FALSE, len = length(models)) + checkmate::check_class(posterior, "postList") + checkmate::check_logical(simple) + + model_names <- unique(gsub("\\d", "", names(models))) + + getModelFit <- ifelse(simple, getModelFitSimple, getModelFitOpt) + + model_fits <- lapply(model_names, getModelFit, dose_levels, posterior, list("scal" = attr(models, "scal"))) + model_fits <- addModelWeights(model_fits) + + names(model_fits) <- model_names + attr(model_fits, "posterior") <- posterior + class(model_fits) <- "modelFits" + + + return (model_fits) + +} + +## ignoring mixture posterior +getModelFitSimple <- function ( + + model, + dose_levels, + posterior, + addArgs = NULL + +) { + + fit <- DoseFinding::fitMod( + dose = dose_levels, + resp = summary.postList(posterior)[, 1], + S = diag(summary.postList(posterior)[, 2]^2), + model = model, + type = "general", + bnds = DoseFinding::defBnds(mD = max(dose_levels))[[model]]) + + pred_vals <- stats::predict(fit, predType = "ls-means") + max_effect <- max(pred_vals) - min(pred_vals) + gAIC <- DoseFinding::gAIC(fit) + + model_fit <- list( + model = model, + coeffs = fit$coefs, + dose_levels = dose_levels, + pred_values = pred_vals, + max_effect = max_effect, + gAIC = gAIC) + + return (model_fit) + +} + +## taking mixture posterior into account +getModelFitOpt <- function ( + + model, + dose_levels, + posterior, + addArgs = NULL + +) { + + getOptParams <- function ( + + model, + dose_levels, + posterior, + addArgs = NULL + + ) { + switch ( + model, + "emax" = { + lb <- c(-Inf, -Inf, 0.001 * max(dose_levels)) + ub <- c(Inf, Inf, 1.5 * max(dose_levels)) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels) / (theta[3] + dose_levels)))^2 / (post_combs$vars[i, ]))) + }, + "sigEmax" = { + lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.5) + ub <- c(Inf, Inf, 1.5 * max(dose_levels), 10) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels^theta[4]) / (theta[3]^theta[4] + dose_levels^theta[4])))^2 / (post_combs$vars[i, ]))) + }, + "exponential" = { + lb <- c(-Inf, -Inf, 0.1 * max(dose_levels)) + ub <- c(Inf, Inf, 2 * max(dose_levels)) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * (exp(dose_levels / theta[3]) - 1)))^2 / (post_combs$vars[i, ]))) + }, + "quadratic" = { + lb <- NULL + ub <- c(Inf, Inf, Inf) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels + theta[3] * dose_levels^2))^2 / (post_combs$vars[i, ]))) + }, + "linear" = { + lb <- NULL + ub <- NULL + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels))^2 / (post_combs$vars[i, ]))) + }, + "logistic" = { + lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.01 * max(dose_levels)) + ub <- c(Inf, Inf, 1.5 * max(dose_levels), 0.5 * max(dose_levels)) + expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] / (1 + exp((theta[3] - dose_levels) / theta[4]))))^2 / (post_combs$vars[i, ]))) + }, + "betaMod" = { + lb <- c(-Inf, -Inf, 0.05, 0.05) + ub <- c(Inf, Inf, 4, 4) + scal <- ifelse(is.null(addArgs), 1.2 * max(dose_levels), addArgs[["scal"]]) #for betaMod shape + expr_i <- substitute( + sum( + (post_combs$means[i, ] - (theta[1] + theta[2] * (((theta[3] + theta[4])^(theta[3] + theta[4])) / ((theta[3] ^ theta[3]) * (theta[4]^theta[4]))) * (dose_levels / scal)^(theta[3]) * ((1 - dose_levels / scal)^(theta[4])))) + ), + list(scal = scal) + ) + }, + stop(paste("error:", model, "shape not yet implemented")) + ) + + simple_fit <- getModelFitSimple( + model = model, + dose_levels = dose_levels, + posterior = posterior) + + param_list <- list( + expr_i = expr_i, + opts = list("algorithm" = "NLOPT_LN_NELDERMEAD", maxeval = 1e3), + params = list( + x0 = simple_fit$coeffs, + lb = lb, + ub = ub), + c_names = names(simple_fit$coeffs)) + + return (param_list) + + } + + optFun <- function ( + theta, + dose_levels, + post_combs, + expr_i + ) { + + comps <- sapply(seq_along(post_combs$weights), function (i) eval(expr_i)) + + return (sum(post_combs$weights * comps)) + + } + + param_list <- getOptParams(model, dose_levels, posterior) + post_combs <- getPostCombsI(posterior) + + fit <- nloptr::nloptr( + eval_f = optFun, + x0 = param_list$params$x0, + lb = param_list$params$lb, + ub = param_list$params$ub, + opts = param_list$opts, + dose_levels = dose_levels, + post_combs = post_combs, + expr_i = param_list$expr_i + ) + + names(fit$solution) <- param_list$c_names + + model_fit <- list( + model = model, + coeffs = fit$solution, + dose_levels = dose_levels) + + model_fit$pred_values <- predictModelFit(model_fit = model_fit, doses = model_fit$dose_levels, addArgs = addArgs) + model_fit$max_effect <- max(model_fit$pred_values) - min(model_fit$pred_values) + model_fit$gAIC <- getGenAIC(model_fit, post_combs) + + return (model_fit) + +} + +predictModelFit <- function ( + + model_fit, + doses = NULL, + addArgs = NULL + +) { + if (is.null(doses)) { + doses <- model_fit$dose_levels + } + pred_vals <- switch ( + model_fit$model, + "emax" = {DoseFinding::emax( + doses, + model_fit$coeffs["e0"], + model_fit$coeffs["eMax"], + model_fit$coeffs["ed50"])}, + "sigEmax" = {DoseFinding::sigEmax( + doses, + model_fit$coeffs["e0"], + model_fit$coeffs["eMax"], + model_fit$coeffs["ed50"], + model_fit$coeffs["h"])}, + "exponential" = {DoseFinding::exponential( + doses, + model_fit$coeffs["e0"], + model_fit$coeffs["e1"], + model_fit$coeffs["delta"])}, + "quadratic" = {DoseFinding::quadratic( + doses, + model_fit$coeffs["e0"], + model_fit$coeffs["b1"], + model_fit$coeffs["b2"])}, + "linear" = {DoseFinding::linear( + doses, + model_fit$coeffs["e0"], + model_fit$coeffs["delta"])}, + "logistic" = {DoseFinding::logistic( + doses, + model_fit$coeffs["e0"], + model_fit$coeffs["eMax"], + model_fit$coeffs["ed50"], + model_fit$coeffs["delta"])}, + "betaMod" = {DoseFinding::betaMod( + doses, + model_fit$coeffs["e0"], + model_fit$coeffs["eMax"], + model_fit$coeffs["delta1"], + model_fit$coeffs["delta2"], + ifelse(is.null(addArgs) | is.null(addArgs[["scal"]]), 1.2 * max(doses), addArgs[["scal"]]))}, + {stop(paste("error:", model_fit$model, "shape not yet implemented"))}) + + return (pred_vals) + +} + +getGenAIC <- function ( + + model_fit, + post_combs + +) { + + expr_i <- quote(sum(1 / post_combs$vars[i, ] * (post_combs$means[i, ] - model_fit$pred_values)^2) + 2 * length(model_fit$coeffs)) + comb_aic <- sapply(seq_along(post_combs$weights), function (i) eval(expr_i)) + + g_aic <- stats::weighted.mean(comb_aic, w = post_combs$weights) + + return (g_aic) + +} + +addModelWeights <- function ( + + model_fits + +) { + g_aics <- sapply(model_fits, function (model_fit) model_fit$gAIC) + exp_values <- exp(-0.5 * g_aics) + model_weights <- exp_values / sum(exp_values) + + names(model_weights) <- NULL + + model_fits_out <- lapply(seq_along(model_fits), function (i) { + + c(model_fits[[i]], model_weight = model_weights[i]) + + }) + + return (model_fits_out) + +} \ No newline at end of file From b21820d63a3bf24131efcdeadbfc9efc4b12d246 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:14:50 +0800 Subject: [PATCH 25/49] updated print method for BayesianMCP object --- R/s3methods.R | 67 +++++++++++++++++++++++++++++++++------------------ 1 file changed, 44 insertions(+), 23 deletions(-) diff --git a/R/s3methods.R b/R/s3methods.R index 7c763c6..5c77c3d 100644 --- a/R/s3methods.R +++ b/R/s3methods.R @@ -48,33 +48,54 @@ print.BayesianMCPMod <- function ( ## BayesianMCP -------------------------------------------- #' @export -print.BayesianMCP <- function ( - - x, - ... +print.BayesianMCP <- function(x, ...) { + cat("Bayesian Multiple Comparison Procedure\n\n") -) { + cat("Summary:\n") + cat(" Sign:", x[1, "sign"], "\n") + cat(" Critical Probability:", x[1, "crit_prob_adj"], "\n") + cat(" Maximum Posterior Probability:", x[1, "max_post_prob"], "\n\n") - n_sim <- nrow(x) + cat("Effective Sample Size (ESS) per Dose Group:\n") + print(attr(x, "ess_avg"), row.names = FALSE) + cat("\n") - cat("Bayesian Multiple Comparison Procedure\n") - - if (n_sim == 1L) { - - attr(x, "crit_prob_adj") <- NULL - attr(x, "success_rate") <- NULL - class(x) <- NULL - - print.default(x, ...) - - } else { - - cat(" Estimated Success Rate: ", attr(x, "successRate"), "\n") - cat(" N Simulations: ", n_sim) - - } + cat("Posterior Probabilities for Model Shapes:\n") + model_probs <- x[1, grep("^post_probs\\.", colnames(x))] + model_names <- gsub("post_probs\\.", "", names(model_probs)) + model_df <- data.frame(Model = model_names, Probability = unlist(model_probs)) + print(model_df, row.names = FALSE) + invisible(x) } +# print.BayesianMCP <- function ( + # +# x, +# ... +# +# ) { +# +# n_sim <- nrow(x) +# +# cat("Bayesian Multiple Comparison Procedure\n") +# +# if (n_sim == 1L) { +# +# attr(x, "crit_prob_adj") <- NULL +# attr(x, "success_rate") <- NULL +# class(x) <- NULL +# +# print.default(x, ...) +# +# } else { +# +# cat(" Estimated Success Rate: ", attr(x, "successRate"), "\n") +# cat(" N Simulations: ", n_sim) +# +# } +# +# } + ## ModelFits ---------------------------------------------- @@ -111,7 +132,7 @@ predict.modelFits <- function ( ... ) { - + predictions <- lapply(object, predictModelFit, doses = doses) attr(predictions, "doses") <- doses From eb3c84658cfb76b5fbfab18a6851ea96326e0b62 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:15:25 +0800 Subject: [PATCH 26/49] change name to modeling --- R/modelling.R | 324 -------------------------------------------------- 1 file changed, 324 deletions(-) delete mode 100644 R/modelling.R diff --git a/R/modelling.R b/R/modelling.R deleted file mode 100644 index 11dfcbe..0000000 --- a/R/modelling.R +++ /dev/null @@ -1,324 +0,0 @@ -#' @title getModelFits -#' -#' @description Fits dose-response curves for the specified dose-response models, based on the posterior distributions. -#' For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions. -#' For the default case, the Nelder-Mead algorithm is used. -#' In detail, for both approaches the mean vector \eqn{\theta^{Y}} and the covariance \eqn{\Sigma} of the (mixture) posterior distributions and the corresponding posterior weights \eqn{\tilde{\omega}_{l}} for \eqn{l \in {1,...,L}} are used as basis -#' For the full fit a GLS estimator is used to minimize the following expression for the respective dose-response models \eqn{m} -#' \deqn{ \hat{\theta}_{m}=argmin_{\theta_{m}} \sum_{l=1}^{L} \tilde{\omega}_{l}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))'\Sigma_{l}^{-1}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))} -#' Therefore the function nloptr of the nloptr package is utilized. -#' In the simplified case \eqn{L=1}, as the dimension of the posterior is reduced to 1 first. -#' The generalized AIC values are calculated via the formula -#' \deqn{gAIC_{m} = \sum_{l=1}^{L} \tilde{\omega}_{l} \sum_{i=0}^{K} \frac{1}{\Sigma_{l_{i,i}}} (\theta_{l_i}^Y - f(dose_{i},\hat{\theta}_{m}))^2 + 2p } -#' where \eqn{p} denotes the number of estimated parameters and \eqn{K} the number of active dose levels. -#' Here as well for the simplified case the formula reduces to one summand as \eqn{L=1}. -#' Corresponding gAIC based weights for model \eqn{M} are calculated as outlined in Schorning et al. (2016) -#' \deqn{ -#' \Omega_I (M) = \frac{\exp(-0.5 gAIC_{M})}{\sum_{m=1}^{Q} \exp(-0.5 gAIC_{m})} -#' } -#' where \eqn{Q} denotes the number of models included in the averaging procedure. -#' @references Schorning K, Bornkamp B, Bretz F, Dette H. 2016. Model selection versus model averaging in dose finding studies. Stat Med; 35; 4021-4040. -#' @param models List of model names for which a fit will be performed. -#' @param dose_levels A vector containing the different dosage levels. -#' @param posterior A getPosterior object, containing the (multivariate) posterior distribution per dosage level. -#' @param simple Boolean variable, defining whether simplified fit will be applied. Default FALSE. -#' @examples -#' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), -#' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), -#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , -#' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , -#' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) -#' models <- c("emax", "exponential", "sigEmax", "linear") -#' dose_levels <- c(0, 1, 2, 4, 8) -#' -#' fit <- getModelFits(models = models, -#' posterior = posterior_list, -#' dose_levels = dose_levels) -#' fit_simple <- getModelFits(models = models, -#' posterior = posterior_list, -#' dose_levels = dose_levels, -#' simple = TRUE) -#' -#' @return An object of class modelFits. A list containing information about the fitted model coefficients, the prediction per dose group as well as maximum effect and generalized AIC (and corresponding weight) per model. -#' -#' @export -getModelFits <- function ( - - models, - dose_levels, - posterior, - simple = FALSE - -) { - - checkmate::check_list(models, any.missing = FALSE) - checkmate::check_double(dose_levels, lower = 0, any.missing = FALSE, len = length(models)) - checkmate::check_class(posterior, "postList") - checkmate::check_logical(simple) - - model_names <- unique(gsub("\\d", "", names(models))) - - getModelFit <- ifelse(simple, getModelFitSimple, getModelFitOpt) - model_fits <- lapply(model_names, getModelFit, dose_levels, posterior, list("scal" = attr(models, "scal"))) - - model_fits <- addModelWeights(model_fits) - - names(model_fits) <- model_names - attr(model_fits, "posterior") <- posterior - class(model_fits) <- "modelFits" - - - return (model_fits) - -} - -## ignoring mixture posterior -getModelFitSimple <- function ( - - model, - dose_levels, - posterior, - addArgs = NULL - -) { - - fit <- DoseFinding::fitMod( - dose = dose_levels, - resp = summary.postList(posterior)[, 1], - S = diag(summary.postList(posterior)[, 2]^2), - model = model, - type = "general", - bnds = DoseFinding::defBnds(mD = max(dose_levels))[[model]]) - - pred_vals <- stats::predict(fit, predType = "ls-means") - max_effect <- max(pred_vals) - min(pred_vals) - gAIC <- DoseFinding::gAIC(fit) - - model_fit <- list( - model = model, - coeffs = fit$coefs, - dose_levels = dose_levels, - pred_values = pred_vals, - max_effect = max_effect, - gAIC = gAIC) - - return (model_fit) - -} - -## taking mixture posterior into account -getModelFitOpt <- function ( - - model, - dose_levels, - posterior, - addArgs = NULL - -) { - - getOptParams <- function ( - - model, - dose_levels, - posterior, - addArgs = NULL - - ) { - switch ( - model, - "emax" = { - lb <- c(-Inf, -Inf, 0.001 * max(dose_levels)) - ub <- c(Inf, Inf, 1.5 * max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels) / (theta[3] + dose_levels)))^2 / (post_combs$vars[i, ]))) - }, - "sigEmax" = { - lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.5) - ub <- c(Inf, Inf, 1.5 * max(dose_levels), 10) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + (theta[2] * dose_levels^theta[4]) / (theta[3]^theta[4] + dose_levels^theta[4])))^2 / (post_combs$vars[i, ]))) - }, - "exponential" = { - lb <- c(-Inf, -Inf, 0.1 * max(dose_levels)) - ub <- c(Inf, Inf, 2 * max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * (exp(dose_levels / theta[3]) - 1)))^2 / (post_combs$vars[i, ]))) - }, - "quadratic" = { - lb <- NULL - ub <- c(Inf, Inf, 0) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels + theta[3] * dose_levels^2))^2 / (post_combs$vars[i, ]))) - }, - "linear" = { - lb <- NULL - ub <- NULL - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] * dose_levels))^2 / (post_combs$vars[i, ]))) - }, - "logistic" = { - lb <- c(-Inf, -Inf, 0.001 * max(dose_levels), 0.01 * max(dose_levels)) - ub <- c(Inf, Inf, 1.5 * max(dose_levels), 0.5 * max(dose_levels)) - expr_i <- quote(sum((post_combs$means[i, ] - (theta[1] + theta[2] / (1 + exp((theta[3] - dose_levels) / theta[4]))))^2 / (post_combs$vars[i, ]))) - }, - "betaMod" = { - lb <- c(-Inf, -Inf, 0.05, 0.05) - ub <- c(Inf, Inf, 4, 4) - scal <- ifelse(is.null(addArgs), 1.2 * max(dose_levels), addArgs[["scal"]]) - expr_i <- substitute( - sum( - (post_combs$means[i, ] - (theta[1] + theta[2] * (((theta[3] + theta[4])^(theta[3] + theta[4])) / ((theta[3] ^ theta[3]) * (theta[4]^theta[4]))) * (dose_levels / scal)^(theta[3]) * ((1 - dose_levels / scal)^(theta[4])))) - ), - list(scal = scal) - ) - }, - stop(paste("error:", model, "shape not yet implemented")) - ) - - simple_fit <- getModelFitSimple( - model = model, - dose_levels = dose_levels, - posterior = posterior) - - param_list <- list( - expr_i = expr_i, - opts = list("algorithm" = "NLOPT_LN_NELDERMEAD", maxeval = 1e3), - params = list( - x0 = simple_fit$coeffs, - lb = lb, - ub = ub), - c_names = names(simple_fit$coeffs)) - - return (param_list) - - } - - optFun <- function ( - - theta, - - dose_levels, - post_combs, - expr_i - - ) { - - comps <- sapply(seq_along(post_combs$weights), function (i) eval(expr_i)) - - return (sum(post_combs$weights * comps)) - - } - - param_list <- getOptParams(model, dose_levels, posterior) - post_combs <- getPostCombsI(posterior) - - fit <- nloptr::nloptr( - eval_f = optFun, - x0 = param_list$params$x0, - lb = param_list$params$lb, - ub = param_list$params$ub, - opts = param_list$opts, - dose_levels = dose_levels, - post_combs = post_combs, - expr_i = param_list$expr_i - ) - - names(fit$solution) <- param_list$c_names - - model_fit <- list( - model = model, - coeffs = fit$solution, - dose_levels = dose_levels) - - model_fit$pred_values <- predictModelFit(model_fit) - model_fit$max_effect <- max(model_fit$pred_values) - min(model_fit$pred_values) - model_fit$gAIC <- getGenAIC(model_fit, post_combs) - - return (model_fit) - -} - -predictModelFit <- function ( - - model_fit, - doses = NULL - -) { - - if (is.null(doses)) { - - doses <- model_fit$dose_levels - - } - - pred_vals <- switch ( - model_fit$model, - "emax" = {DoseFinding::emax( - doses, - model_fit$coeffs["e0"], - model_fit$coeffs["eMax"], - model_fit$coeffs["ed50"])}, - "sigEmax" = {DoseFinding::sigEmax( - doses, - model_fit$coeffs["e0"], - model_fit$coeffs["eMax"], - model_fit$coeffs["ed50"], - model_fit$coeffs["h"])}, - "exponential" = {DoseFinding::exponential( - doses, - model_fit$coeffs["e0"], - model_fit$coeffs["e1"], - model_fit$coeffs["delta"])}, - "quadratic" = {DoseFinding::quadratic( - doses, - model_fit$coeffs["e0"], - model_fit$coeffs["b1"], - model_fit$coeffs["b2"])}, - "linear" = {DoseFinding::linear( - doses, - model_fit$coeffs["e0"], - model_fit$coeffs["delta"])}, - "logistic" = {DoseFinding::logistic( - doses, - model_fit$coeffs["e0"], - model_fit$coeffs["eMax"], - model_fit$coeffs["ed50"], - model_fit$coeffs["delta"])}, - {stop(paste("error:", model_fit$model, "shape not yet implemented"))}) - - return (pred_vals) - -} - -getGenAIC <- function ( - - model_fit, - post_combs - -) { - - expr_i <- quote(sum(1 / post_combs$vars[i, ] * (post_combs$means[i, ] - model_fit$pred_values)^2) + 2 * length(model_fit$coeffs)) - comb_aic <- sapply(seq_along(post_combs$weights), function (i) eval(expr_i)) - - g_aic <- stats::weighted.mean(comb_aic, w = post_combs$weights) - - return (g_aic) - -} - -addModelWeights <- function ( - - model_fits - -) { - - g_aics <- sapply(model_fits, function (model_fit) model_fit$gAIC) - exp_values <- exp(-0.5 * g_aics) - model_weights <- exp_values / sum(exp_values) - - names(model_weights) <- NULL - - model_fits_out <- lapply(seq_along(model_fits), function (i) { - - c(model_fits[[i]], model_weight = model_weights[i]) - - }) - - return (model_fits_out) - -} \ No newline at end of file From 8a35435753cdefcaa64ce79c9183dc6696c2f0b1 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:15:39 +0800 Subject: [PATCH 27/49] updated wordlist --- inst/WORDLIST | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 55 insertions(+), 1 deletion(-) diff --git a/inst/WORDLIST b/inst/WORDLIST index 2bded23..af9ba59 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -1,5 +1,59 @@ +Bjoern +Bornkamp +Bretz +CRC +Codecov +Dette +DoseFinding +GLS +Gierse +Iasonos +Kunzmann +LinLog +Loley +MCP MCPMod +Macos +NCT +Nelder +O'Quigley Pinheiro +RBesT Schmidli +Schorning +Thoma +Thomann +Un +Yao al -et \ No newline at end of file +assessDesign +betaMod +biom +critVal +doi +emax +ess +et +gAIC +getBootstrapQuantiles +getContr +getCritProb +getESS +getModelFits +getPosterior +ggplot +mixnorm +modelFits +nloptr +normMix +optContr +performBayesianMCP +performBayesianMCPMod +postList +postmix +pst +rmix +sd +se +simulateData +summand From ce2a0a8f374859455fc547e8548d4ff781b59be7 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:16:22 +0800 Subject: [PATCH 28/49] updated docs --- man/getModelFits.Rd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/man/getModelFits.Rd b/man/getModelFits.Rd index 3c7eab2..8e2023d 100644 --- a/man/getModelFits.Rd +++ b/man/getModelFits.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/modelling.R +% Please edit documentation in R/modeling.R \name{getModelFits} \alias{getModelFits} \title{getModelFits} From e750aa92331496fb842633b430f0b496a82172be Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:20:16 +0800 Subject: [PATCH 29/49] spelling --- man/performBayesianMCPMod.Rd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/man/performBayesianMCPMod.Rd b/man/performBayesianMCPMod.Rd index ea69c93..d2fb698 100644 --- a/man/performBayesianMCPMod.Rd +++ b/man/performBayesianMCPMod.Rd @@ -16,10 +16,10 @@ performBayesianMCPMod(posterior_list, contr, crit_prob_adj, simple = FALSE) \item{simple}{Boolean variable, defining whether simplified fit will be applied. Passed to the getModelFits() function. Default FALSE.} } \value{ -Bayesian MCP test result as well as modelling result. +Bayesian MCP test result as well as modeling result. } \description{ -Performs Bayesian MCP Test step and modeling in a combined fashion. See performBayesianMCP() function for MCP Test step and getModelFits() for the modelling step +Performs Bayesian MCP Test step and modeling in a combined fashion. See performBayesianMCP() function for MCP Test step and getModelFits() for the modeling step } \examples{ mods <- DoseFinding::Mods(linear = NULL, From 18a638612214449d38ffbece06a12e27dff08e47 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Sun, 28 Apr 2024 17:21:10 +0800 Subject: [PATCH 30/49] updated vignette to be more concise --- vignettes/analysis_normal.qmd | 166 +++++++++------------------------- 1 file changed, 41 insertions(+), 125 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index bbd4446..edea10b 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -86,55 +86,33 @@ library(reactable) set.seed(7015) ``` -# Background and data +# Introduction -In this vignette we will show the use of the `{BayesianMCPMod}` package for the analysis -of a phase 2 dose-finding trial. +This vignette demonstrates the application of the {BayesianMCPMod} package for +analyzing a phase 2 dose-finding trial using the Bayesian MCPMod approach. -Since we're working in the Bayesian framework, we need to specify a prior. +# Prior Specification Ideally, priors are grounded in historical data. This approach allows for the synthesis of prior knowledge with current data, enhancing the accuracy of trial evaluations. -The focus of this vignette is more generic, however. We utilize a weakly-informative -prior for the Bayesian MCPMod evaluation of a phase 2 trial. - -# Weakly-Informative Prior Specification - -Rather than constructing a complex mixture prior for the control group and -differentiating active groups with detailed priors, we will specify weakly-informative -priors across all dose groups. This simplification aims to underscore the -flexibility in prior selection and its impact on the Bayesian analysis framework. - -Weakly-informative priors, often chosen for their neutrality, offer a baseline -from which the trial data can speak more prominently. This approach is particularly -useful in early-stage research or when historical data is limited or not directly applicable. -It also allows one to add in external data when/if it becomes available. +The focus of this vignette is more generic, however. We specify weakly-informative +priors across all dose groups to allow the trial data to have a stronger influence on the analysis. ```{r} dose_levels <- c(0, 2.5, 5, 10) prior_list <- lapply(dose_levels, function(dose_group) { - # weakly informative normal distribution RBesT::mixnorm(weak = c(w = 1, m = 0, s = 200), sigma = 10) }) names(prior_list) <- c("Ctr", paste0("DG_", dose_levels[-1])) ``` -# Specifications for the new trial - -## Specifying dose-response model shapes - -To be able to apply the Bayesian MCPMod approach, candidate models need to be -specified using functions from the R package DoseFinding. - -In a real life scenario, we would typically only include a few candidate models, maybe 4 or 5. - -For the purpose of the exposition, we will generate a greater variety of models to demonstrate -different approaches to the problem. Model shapes can be parameterized using guesstimates -based on limited information as well as directly providing the appropriate distribution parameters. +# Dose-Response Model Shapes +Candidate models are specified using the {DoseFinding} package. Models can be +parameterized using guesstimates or by directly providing distribution parameters. Note that the linear candidate model does not require parameterization. [**Note:** The LinLog model is rarely used and not currently supported by `{BayesianMCPMod}`.]{.aside} @@ -167,7 +145,7 @@ In some cases, you need to provide more information. For instance, `sigEmax` requires a pair of `d` and `p` values, and `exponential` requires the specification of the maximum dose for the trial (`Maxd`). -See the help files for model specifications by typing `?DoseFinding::guesst` in your console. +[See the help files for model specifications by typing `?DoseFinding::guesst` in your console]{.aside} The output of this function is a parameterization of the dose-response model. For example, the parameters for the `sigEmax_guesst` are `r sigEmax_guesst` and would correspond to the last @@ -183,11 +161,6 @@ DoseFinding::sigEmax( ) ``` -Essentially `{DoseFinding}` is providing an abstraction that allows us to use -clinically meaningful and relatable variables to specify the model shape. This makes -it much easier to go from discussions within the trial team to the implementation of -the MCPMod framework. - Of course, you can also specify the models directly on the parameter scale (without using `DoseFinding::guesst`). For example, you can get a betaMod model by specifying `delta1` and `delta2` @@ -198,9 +171,6 @@ betaMod_params <- c(delta1 = 1, delta2 = 1) quadratic_params <- c(delta2 = -0.1) ``` -Again, you can reference the `{DoseFinding}` package's help files for how to parameterize -the dose-response models. - Now, we can go ahead and create a `Mods` object, which will be used in the remainder of the vignette. @@ -237,13 +207,10 @@ And we can see the assumed treatment effects for the specified dose groups below knitr::kable(DoseFinding::getResp(mods, doses = dose_levels)) ``` -## Trial data for the example +## Trial Data -We will use the trial with ct.gov number NCT00735709 as our phase 2 trial data -that we'd like to apply the Bayesian MCPMod approach to [@nct00735709_2024a]. - -This dataset comes from the `{clinDR}` package. Make sure you have it installed -before running the code below. +We will use the trial with ct.gov number NCT00735709 as our phase 2 trial data, +available in the `{clinDR}` package [@nct00735709_2024a]. ```{r} data("metaData") @@ -258,15 +225,11 @@ trial_data <- dplyr::filter( n_patients <- c(128, 124, 129, 122) ``` -# Combination of prior information and trial results +# Posterior Calculation In the first step of Bayesian MCPMod, the posterior is calculated by combining the prior information with the estimated results of the trial [@fleischer_2022]. -Via the summary function, it is possible to print out the summary information of -the posterior distributions. Remember that our `prior_list` object is defined above -using a weakly-informative prior. - ```{r} posterior <- BayesianMCPMod::getPosterior( prior_list = prior_list, @@ -278,15 +241,13 @@ posterior <- BayesianMCPMod::getPosterior( knitr::kable(summary(posterior)) ``` -# Execution of the Bayesian MCPMod test step +# Bayesian MCPMod Test Step -For the execution of the testing step of Bayesian MCPMod, a critical value on the -probability scale is determined for a given alpha level. +The testing step of Bayesian MCPMod is executed using a critical value on the +probability scale and a pseudo-optimal contrast matrix. -This critical value is calculated using the re-estimated contrasts for the -frequentist MCPMod to ensure that, when using weakly-informative priors, -the actual error level for falsely declaring a significant trial in is controlled -by the specified alpha level. +The critical value is calculated using re-estimated contrasts for frequentist +MCPMod to ensure error control when using weakly-informative priors. A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see [@fleischer_2022] for more details). @@ -332,8 +293,7 @@ contr_mat_prior <- BayesianMCPMod::getContr( prior_list = prior_list) ``` -The Bayesian MCP testing step is then executed based on the posterior information, -the provided contrasts and the multiplicity adjusted critical value. +The Bayesian MCP testing step is then executed: ```{r} BMCP_result <- BayesianMCPMod::performBayesianMCP( @@ -342,29 +302,17 @@ BMCP_result <- BayesianMCPMod::performBayesianMCP( crit_prob_adj = crit_pval) ``` -Summary information +Summary information: ```{r} -display_params_table(BMCP_result[1, c(1:3)]) +BMCP_result ``` -Effective sample size (ESS) for each dose group +The testing step is significant, indicating a non-flat dose-response shape. +All models are significant, with the `emax` model indicating the greatest deviation +from the null hypothesis. -```{r} -display_params_table(attr(BMCP_result, "ess_avg")) -``` - -Posterior probabilities for each model shape - -```{r} -display_params_table((BMCP_result[1, c(4:ncol(BMCP_result))])) -``` - -The testing step is significant indicating a non-flat dose-response shape. -Further, all models are significant and the p-value for the `emax` model -indicates the greatest deviation from the null hypothesis. - -# Model fitting and visualization of results +# Model Fitting and Visualization In the model fitting step the posterior distribution is used as basis. @@ -381,8 +329,8 @@ non-linear optimization problem is addressed via the Nelder-Mead algorithm The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. -For the considered case, the simplified and the full fit are very similar, so we'll -just present the full fit. +For the considered case, the simplified and the full fit are very similar, so we +present the full fit. ```{r} # If simple = TRUE, uses approx posterior @@ -394,15 +342,13 @@ fit <- BayesianMCPMod::getModelFits( simple = FALSE) ``` -Via the `stats::predict()` function, one can also receive estimates for dose levels that -were not included in the trial. +Estimates for dose levels not included in the trial: ```{r} display_params_table(stats::predict(fit, doses = c(0, 2.5, 4, 5, 7, 10))) ``` -It is possible to plot the fitted dose response models and an AIC based average -model (black lines). +Plots of fitted dose-response models and an AIC-based average model: ```{r} plot(fit) @@ -419,7 +365,6 @@ simplified fitting step and a prediction is performed. - These predictions are then used to identify and visualize the specified quantiles. ```{r} -#| cache: true plot(fit, cr_bands = TRUE) ``` @@ -440,59 +385,30 @@ bootstrap_quantiles <- BayesianMCPMod::getBootstrapQuantiles( ```{r} #| code-fold: true reactable::reactable( - data = bootstrap_quantiles, - groupBy = "models", + data = bootstrap_quantiles, + groupBy = "models", columns = list( - doses = colDef( - aggregate = "count", - format = list( - aggregated = colFormat(suffix = " doses") - ) - ), - "2.5%" = colDef( - aggregate = "mean", - format = list( - aggregated = colFormat(prefix = "mean = ", digits = 2), - cell = colFormat(digits = 4) - ) - ), - "50%" = colDef( - aggregate = "mean", - format = list( - aggregated = colFormat(prefix = "mean = ", digits = 2), - cell = colFormat(digits = 4) - ) - ), - "97.5%" = colDef( - aggregate = "mean", - format = list( - aggregated = colFormat(prefix = "mean = ", digits = 2), - cell = colFormat(digits = 4) - ) - ) + doses = colDef(aggregate = "count", format = list(aggregated = colFormat(suffix = " doses"))), + "2.5%" = colDef(aggregate = "mean", format = list(aggregated = colFormat(prefix = "mean = ", digits = 2), cell = colFormat(digits = 4))), + "50%" = colDef(aggregate = "mean", format = list(aggregated = colFormat(prefix = "mean = ", digits = 2), cell = colFormat(digits = 4))), + "97.5%" = colDef(aggregate = "mean", format = list(aggregated = colFormat(prefix = "mean = ", digits = 2), cell = colFormat(digits = 4))) ) ) ``` -Technical note: The median quantile of the bootstrap based procedure is not +**Technical note:** The median quantile of the bootstrap based procedure is not necessary similar to the main model fit, as they are derived via different procedures. -The main fit, i.e. the black lines in the plot, show the best fit of a certain model -based on minimizing the residuals for the posterior distribution, while the bootstrap -based 50% quantile shows the median fit of the random sampling and fitting procedure. +The main fit (black line) minimizes residuals for the posterior distribution, +while the bootstrap median is the median fit of random sampling. # Additional note -It is also possible to perform the testing and modeling step in a combined fashion -via the `performBayesianMCPMod()` function. - -This code serves merely as an example and is not run in this vignette. - -[TODO: debug error, see above about nloptr]{.aside} +Testing and modeling can also be combined via `performBayesianMCPMod()`, +but this is not run here. ```{r} #| eval: false - BayesianMCPMod::performBayesianMCPMod( posterior_list = posterior, contr = contr_mat, From 129d744d88dbffe2880fb57d4e2a01a146b3423d Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Mon, 29 Apr 2024 10:11:12 +0800 Subject: [PATCH 31/49] fixed bug in getBootstrapQuantiles that didnt return level for betaMod factor --- R/bootstrapping.R | 13 +++++-------- vignettes/analysis_normal.qmd | 29 ++++++++++++++++++----------- 2 files changed, 23 insertions(+), 19 deletions(-) diff --git a/R/bootstrapping.R b/R/bootstrapping.R index a48bce5..a6afac6 100644 --- a/R/bootstrapping.R +++ b/R/bootstrapping.R @@ -34,13 +34,13 @@ #' #' @export getBootstrapQuantiles <- function ( - + model_fits, quantiles, n_samples = 1e3, doses = NULL, avg_fit = TRUE - + ) { mu_hat_samples <- sapply(attr(model_fits, "posterior"), @@ -105,13 +105,10 @@ getBootstrapQuantiles <- function ( cr_bounds_data <- cbind( doses = doses, models = rep( - x = factor(model_names, - levels = c("linear", "emax", "exponential", - "sigEmax", "logistic", "quadratic", - "avgFit")), + x = factor(model_names, levels = model_names), each = length(doses)), as.data.frame(quant_mat)) - + return (cr_bounds_data) - + } diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index edea10b..6899fb1 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -5,21 +5,21 @@ date: today format: html: fig-height: 3.5 - code-fold: show self-contained: true toc: true number-sections: true bibliography: references.bib vignette: > %\VignetteIndexEntry{Analysis Example of Bayesian MCPMod for Continuous Data} - %\VignetteEncoding{UTF-8} - %\VignetteEngine{quarto::html} -editor_options: - chunk_output_type: console + %\VignetteEncoding{UTF-8} + %\VignetteEngine{quarto::html} --- ```{r} -#| include: false +#| code-summary: setup +#| code-fold: true +#| message: false +#| warning: false #' Display Parameters Table #' @@ -56,6 +56,18 @@ display_params_table <- function(named_list) { display_table <- rbind(display_table, values) } + round_numeric <- function(x, digits = 3) { + if (is.numeric(x)) { + return(round(x, digits)) + } else { + return(x) + } + } + + display_table[1] <- lapply(display_table[1], function(sublist) { + lapply(sublist, round_numeric) + }) + class(display_table[[1]]) <- "list" if (nrow(value_names) == 0) { @@ -70,11 +82,6 @@ display_params_table <- function(named_list) { ) } } -``` - -```{r} -#| message: false -#| include: false library(BayesianMCPMod) library(RBesT) From fd3fad753389851bdb983e6da3d018a0a0791318 Mon Sep 17 00:00:00 2001 From: Steven Brooks Date: Mon, 29 Apr 2024 10:19:54 +0800 Subject: [PATCH 32/49] ... --- R/plot.R | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/R/plot.R b/R/plot.R index 155468e..747ef9c 100644 --- a/R/plot.R +++ b/R/plot.R @@ -87,11 +87,8 @@ plot.modelFits <- function ( gg_data <- data.frame( doses = rep(dose_seq, length(model_names)), fits = as.vector(preds_models), - models = rep(factor(model_names, - levels = c("linear", "emax", "exponential", - "sigEmax", "logistic", "quadratic", - "avgFit")), - each = plot_res)) + models = rep(factor(model_names, levels = model_names), + each = plot_res)) if (gAIC) { @@ -150,7 +147,7 @@ plot.modelFits <- function ( x = -Inf, y = Inf, hjust = "inward", vjust = "inward", size = 3) - } + } } if (cr_bands) { @@ -195,7 +192,7 @@ plot.modelFits <- function ( width = 0, alpha = 0.5) - } + } } + ## Posterior Medians ggplot2::geom_point( From e4b161213a7db979bf9cee5b011bf42f402a5b72 Mon Sep 17 00:00:00 2001 From: Bossert Date: Wed, 12 Jun 2024 12:06:55 +0100 Subject: [PATCH 33/49] correction of bug in beta function and inclusion of beta and quadratic in plot function --- R/modeling.R | 6 +++--- R/plot.R | 4 +++- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/R/modeling.R b/R/modeling.R index 28c90a8..132b596 100644 --- a/R/modeling.R +++ b/R/modeling.R @@ -164,8 +164,8 @@ getModelFitOpt <- function ( scal <- ifelse(is.null(addArgs), 1.2 * max(dose_levels), addArgs[["scal"]]) #for betaMod shape expr_i <- substitute( sum( - (post_combs$means[i, ] - (theta[1] + theta[2] * (((theta[3] + theta[4])^(theta[3] + theta[4])) / ((theta[3] ^ theta[3]) * (theta[4]^theta[4]))) * (dose_levels / scal)^(theta[3]) * ((1 - dose_levels / scal)^(theta[4])))) - ), + ((post_combs$means[i, ] - (theta[1] + theta[2] * (((theta[3] + theta[4])^(theta[3] + theta[4])) / ((theta[3] ^ theta[3]) * (theta[4]^theta[4]))) * (dose_levels / scal)^(theta[3]) * ((1 - dose_levels / scal)^(theta[4]))))^2 / (post_combs$vars[i, ]))) + , list(scal = scal) ) }, @@ -179,7 +179,7 @@ getModelFitOpt <- function ( param_list <- list( expr_i = expr_i, - opts = list("algorithm" = "NLOPT_LN_NELDERMEAD", maxeval = 1e3), + opts = list("algorithm" = "NLOPT_LN_NELDERMEAD", maxeval = 1e3), #,stopval=0 params = list( x0 = simple_fit$coeffs, lb = lb, diff --git a/R/plot.R b/R/plot.R index 747ef9c..f107dc6 100644 --- a/R/plot.R +++ b/R/plot.R @@ -103,7 +103,9 @@ plot.modelFits <- function ( gsub("quadratic", "quad", x = _) |> gsub("linear", "lin", x = _) |> gsub("logistic", "log", x = _) |> - gsub("sigEmax", "sigE", x = _) + gsub("sigEmax", "sigE", x = _) |> + gsub("betaMod", "betaM", x = _)|> + gsub("quadratic", "quad", x = _) label_avg <- paste0(paste_names, "=", round(mod_weights, 1), collapse = ", ") From 1c86aa91f1f7d89acec14f83a61cabdadea4c622 Mon Sep 17 00:00:00 2001 From: Bossert Date: Mon, 24 Jun 2024 10:42:58 +0100 Subject: [PATCH 34/49] Minor changes in the formulation and deletion of one code chunk --- vignettes/analysis_normal.qmd | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 6899fb1..0b13e4c 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -154,24 +154,12 @@ the maximum dose for the trial (`Maxd`). [See the help files for model specifications by typing `?DoseFinding::guesst` in your console]{.aside} -The output of this function is a parameterization of the dose-response model. For example, the -parameters for the `sigEmax_guesst` are `r sigEmax_guesst` and would correspond to the last -two parameters in the `DoseFinding::sigEmax` function, e.g., -```{r} -DoseFinding::sigEmax( - dose = c(2.5, 5), # dose levels - e0 = 0, # placebo effect - eMax = -1, # max effect - ed50 = sigEmax_guesst[["ed50"]], # dose that yields half of eMax - h = sigEmax_guesst[["h"]] # Hill parameter (steepness at ed50) -) -``` Of course, you can also specify the models directly on the parameter scale (without using `DoseFinding::guesst`). For example, you can get a betaMod model by specifying `delta1` and `delta2` -parameters (`scale` is assumed to be `1.2`), or a quadratic model with the `delta2` parameter. +parameters (`scale` is assumed to be `1.2` of the maximum dose), or a quadratic model with the `delta2` parameter. ```{r} betaMod_params <- c(delta1 = 1, delta2 = 1) @@ -253,7 +241,7 @@ knitr::kable(summary(posterior)) The testing step of Bayesian MCPMod is executed using a critical value on the probability scale and a pseudo-optimal contrast matrix. -The critical value is calculated using re-estimated contrasts for frequentist +The critical value is calculated using (re-estimated) contrasts for frequentist MCPMod to ensure error control when using weakly-informative priors. A pseudo-optimal contrast matrix is generated based on the variability of the From f2ba43b5c1c8677c04c2f4ebbf75be6e570a4312 Mon Sep 17 00:00:00 2001 From: "Kleibrink,Gina_Marie (MED BDS) BIP-DE-B" Date: Thu, 27 Jun 2024 09:19:52 +0100 Subject: [PATCH 35/49] test --- vignettes/analysis_normal.qmd | 1 + 1 file changed, 1 insertion(+) diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal.qmd index 0b13e4c..6124a67 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal.qmd @@ -128,6 +128,7 @@ In the code below, the models are "guesstimated" using the `DoseFinding::guesst` The `d` option usually takes a single value (a dose level), and the corresponding `p` for the maximum effect achieved at `d`. + ```{r} # Guesstimate estimation exp_guesst <- DoseFinding::guesst( From 9f5db724352779c720be0969ff5727163f1486b8 Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 10:48:05 +0200 Subject: [PATCH 36/49] readd vignettes --- vignettes/Simulation_Example.Rmd | 163 +++++++++++++++++ vignettes/analysis_normal.Rmd | 288 +++++++++++++++++++++++++++++++ 2 files changed, 451 insertions(+) create mode 100644 vignettes/Simulation_Example.Rmd create mode 100644 vignettes/analysis_normal.Rmd diff --git a/vignettes/Simulation_Example.Rmd b/vignettes/Simulation_Example.Rmd new file mode 100644 index 0000000..4b36538 --- /dev/null +++ b/vignettes/Simulation_Example.Rmd @@ -0,0 +1,163 @@ +--- +title: "Simulation Example of Bayesian MCPMod for Continuous Data" +output: rmarkdown::html_vignette +number_sections: true +vignette: > + %\VignetteIndexEntry{Simulation Example of Bayesian MCPMod for Continuous Data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +```{r setup} +library(BayesianMCPMod) +library(clinDR) +library(dplyr) + +set.seed(7015) +``` + +# Background and data + +In this vignette, we will show the use of the Bayesian MCPMod package for trial planning for continuous distributed data. +As in [the analysis example vignette](analysis_normal.html), we focus on the indication MDD and make use of historical data that is included in the clinDR package. +More specifically, trial results for BRINTELLIX will be utilized to establish an informative prior for the control group. + +# Calculation of a MAP prior +In a first step, a meta analytic predictive prior will be calculated using historical data from 5 trials with main endpoint Change from baseline in MADRS score after 8 weeks. +Please note that only information from the control group will be integrated leading to an informative mixture prior for the control group, while for the active groups a non-informative prior will be specified. + +```{r Historical Data} +data("metaData") +testdata <- as.data.frame(metaData) +dataset <- filter(testdata, bname == "BRINTELLIX") +histcontrol <- filter(dataset, dose == 0, primtime == 8, indication == "MAJOR DEPRESSIVE DISORDER") + +hist_data <- data.frame( + trial = histcontrol$nctno, + est = histcontrol$rslt, + se = histcontrol$se, + sd = histcontrol$sd, + n = histcontrol$sampsize) + +sd_tot <- with(hist_data, sum(sd * n) / sum(n)) +``` +We will make use of the same getPriorList() function as in the [analysis example vignette](analysis_normal.html) to create a MAP prior. +```{r Setting Prior without execution, eval = FALSE} +dose_levels <- c(0, 2.5, 5, 10, 20) + +prior_list <- getPriorList( + hist_data = hist_data, + dose_levels = dose_levels, + robust_weight = 0.3) +``` +```{r Setting Prior, echo = FALSE} +dose_levels <- c(0, 2.5, 5, 10, 20) + +prior_list <- list( + Ctr = RBesT::mixnorm( + comp1 = c(w = 0.446213, m = -12.774661, s = 1.393130), + comp1 = c(w = 0.253787, m = 3.148116, s = 3.148116), + robust = c(w = 0.3, m = 9.425139, s = 9.425139), + sigma = sd_tot), + DG_1 = RBesT::mixnorm( + comp1 = c(w = 1, m = -12.816875, n = 1), + sigma = sd_tot, + param = "mn"), + DG_2 = RBesT::mixnorm( + comp1 = c(w = 1, m = -12.816875, n = 1), + sigma = sd_tot, + param = "mn"), + DG_3 = RBesT::mixnorm( + comp1 = c(w = 1, m = -12.816875, n = 1), + sigma = sd_tot, + param = "mn"), + DG_4 = RBesT::mixnorm( + comp1 = c(w = 1, m = -12.816875, n = 1), + sigma = sd_tot, + param = "mn") +) +``` + +# Specification of new trial design + +For the hypothetical new trial, we plan with 4 active dose levels \eqn{2.5, 5, 10, 20} and we specify a broad set of potential dose-response relationships, including a linear, an exponential, an emax and 2 sigEMax models. +Furthermore, we assume a maximum effect of -3 on top of control (i.e. assuming that active treatment can reduce the MADRS score after 8 weeks by up to 15.8) and plan a trial with 80 patients for all active groups and 60 patients for control. +```{r} +exp <- DoseFinding::guesst( + d = 5, + p = c(0.2), + model = "exponential", + Maxd = max(dose_levels)) + +emax <- DoseFinding::guesst( + d = 2.5, + p = c(0.9), + model = "emax") + +sigemax <- DoseFinding::guesst( + d = c(2.5, 5), + p = c(0.1, 0.6), + model = "sigEmax") + +sigemax2 <- DoseFinding::guesst( + d = c(2, 4), + p = c(0.3, 0.8), + model = "sigEmax") + +mods <- DoseFinding::Mods( + linear = NULL, + emax = emax, + exponential = exp, + sigEmax = rbind(sigemax, sigemax2), + doses = dose_levels, + maxEff = -3, + placEff = -12.8) + +n_patients <- c(60, 80, 80, 80, 80) +``` + +# Calculation of success probabilities + +To calculate success probabilities for the different assumed dose-response models and the specified trial design we will apply the assessDesign function. +For illustration purposes, the number of simulated trial results is reduced to 100 in this example. +```{r} +success_probabilities <- assessDesign( + n_patients = n_patients, + mods = mods, + prior_list = prior_list, + sd = sd_tot, + n_sim = 100) # speed up example run-time + +success_probabilities +``` +As an alternative, we will evaluate a design with the same overall sample size but allocating more patients on the highest dose group and control. +```{r} +success_probabilities_uneq <- assessDesign( + n_patients = c(80, 60, 60, 60, 120), + mods = mods, + prior_list = prior_list, + sd = sd_tot, + n_sim = 100) # speed up example run-time +success_probabilities_uneq +``` +For this specific trial setting the adapted allocation ratio leads to increased success probabilities under all assumed dose response relationships. + +Instead of specifying the assumed effects via the models it is also possible to directly specify the effects for the individual dose levels via the dr_means input. +This allows e.g. also the simulation of scenarios with a prior-data conflict. +```{r} +success_probabilities <- assessDesign( + n_patients = c(60, 80, 80, 80, 80), + mods = mods, + prior_list = prior_list, + sd = sd_tot, + dr_means = c(-12, -14, -15, -16, -17), + n_sim = 100) # speed up example run-time +success_probabilities +``` \ No newline at end of file diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd new file mode 100644 index 0000000..af6159f --- /dev/null +++ b/vignettes/analysis_normal.Rmd @@ -0,0 +1,288 @@ +--- +title: "Analysis Example of Bayesian MCPMod for Continuous Data" +output: rmarkdown::html_vignette +number_sections: true +vignette: > + %\VignetteIndexEntry{Analysis Example of Bayesian MCPMod for Continuous Data} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +```{r setup} +library(BayesianMCPMod) +library(clinDR) +library(dplyr) + +set.seed(7015) +``` + +# Background and data + +In this vignette we will show the use of the Bayesian MCPMod package for continuous distributed data. +The focus lies on the utilization of an informative prior and the Bayesian MCPMod evaluation of a single trial. +We will use data that is included in the clinDR package. +More specifically, trial results of BRINTELLIX will be used to illustrate the specification of an informative prior and the usage of such a prior for the Bayesian evaluation of a (hypothetical) new trial. +BRINTELLIX is a medication used to treat major depressive disorder. +Various clinical trials with different dose groups, including control groups, were conducted. + +# Calculation of a MAP prior + +In a first step, a meta analytic prior will be calculated using historical data from 4 trials with main endpoint Change from baseline in MADRS score after 8 weeks. +Please note that only information from the control group will be integrated leading to an informative mixture prior for the control group, while for the active groups a non-informative prior will be specified. +```{r Historical Data for Control Arm} +data("metaData") +dataset <- filter(as.data.frame(metaData), bname == "BRINTELLIX") +histcontrol <- filter( + dataset, + dose == 0, + primtime == 8, + indication == "MAJOR DEPRESSIVE DISORDER", + protid != 5) + +hist_data <- data.frame( + trial = histcontrol$nctno, + est = histcontrol$rslt, + se = histcontrol$se, + sd = histcontrol$sd, + n = histcontrol$sampsize) +``` +Here, we suggest a function to construct a list of prior distributions for the different dose groups. +This function is adapted to the needs of this example. +Other applications may need a different way to construct prior distributions. +```{r Defining MAP prior function} +getPriorList <- function ( + + hist_data, + dose_levels, + dose_names = NULL, + robust_weight = 0.5 + +) { + + sd_tot <- with(hist_data, sum(sd * n) / sum(n)) + + gmap <- RBesT::gMAP( + formula = cbind(est, se) ~ 1 | trial, + weights = hist_data$n, + data = hist_data, + family = gaussian, + beta.prior = cbind(0, 100 * sd_tot), + tau.dist = "HalfNormal", + tau.prior = cbind(0, sd_tot / 4)) + + prior_ctr <- RBesT::automixfit(gmap) + + if (!is.null(robust_weight)) { + + prior_ctr <- suppressMessages(RBesT::robustify( + priormix = prior_ctr, + weight = robust_weight, + sigma = sd_tot)) + + } + + prior_trt <- RBesT::mixnorm( + comp1 = c(w = 1, m = summary(prior_ctr)[1], n = 1), + sigma = sd_tot, + param = "mn") + + prior_list <- c(list(prior_ctr), + rep(x = list(prior_trt), + times = length(dose_levels[-1]))) + + if (is.null(dose_names)) { + + dose_names <- c("Ctr", paste0("DG_", seq_along(dose_levels[-1]))) + + } + + names(prior_list) <- dose_names + + return (prior_list) + +} +``` +With the dose levels to be investigated, the prior distribution can be constructed. +```{r Getting the MAP prior} +dose_levels <- c(0, 2.5, 5, 10) + +prior_list <- getPriorList( + hist_data = hist_data, + dose_levels = dose_levels, + robust_weight = 0.3) + +getESS(prior_list) +``` + +# Specifications for the new trial + +To be able to apply the Bayesian MCPMod approach, candidate models need to be specified using functions from the R package DoseFinding. +Since there are only 3 active dose levels we will limit the set of candidate models to a linear, an exponential, and an emax model. +Note that the linear candidate model does not require a guesstimate. +```{r Pre-Specification of candidate models} +exp_guesst <- DoseFinding::guesst( + d = 5, + p = c(0.2), + model = "exponential", + Maxd = max(dose_levels)) + +emax_guesst <- DoseFinding::guesst( + d = 2.5, + p = c(0.9), + model = "emax") + +mods <- DoseFinding::Mods( + linear = NULL, + emax = emax_guesst, + exponential = exp_guesst, + doses = dose_levels, + maxEff = -1, + placEff = -12.8) +``` +We will use the trial with ct.gov number NCT00735709 as exemplary new trial. +```{r new trial} +new_trial <- filter( + dataset, + primtime == 8, + indication == "MAJOR DEPRESSIVE DISORDER", + protid == 5) + +n_patients <- c(128, 124, 129, 122) +``` +# Combination of prior information and trial results +As outlined in Fleischer et al. [Bayesian MCPMod. Pharmaceutical Statistics. 2022; 21(3): 654-670.], in a first step the posterior is calculated combining the prior information with the estimated results of the new trial. Via the summary function it is possible to print out the summary information of the posterior distributions. +```{r Trial results} +posterior <- getPosterior( + prior = prior_list, + mu_hat = new_trial$rslt, + se_hat = new_trial$se, + calc_ess = TRUE) + +summary(posterior) +``` +# Execution of Bayesian MCPMod Test step + +For the execution of the testing step of Bayesian MCPMod a critical value on the probability scale will be determined for a given alpha level. +This critical value is calculated using the re-estimated contrasts for the frequentist MCPMod to ensure that, when using non-informative priors, the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled by the specified alpha level. + +A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see Fleischer et al. 2022 for more details). +```{r Preparation of input for Bayesian MCPMod Test step} +crit_pval <- getCritProb( + mods = mods, + dose_levels = dose_levels, + se_new_trial = new_trial$se, + alpha_crit_val = 0.05) + +contr_mat <- getContr( + mods = mods, + dose_levels = dose_levels, + sd_posterior = summary(posterior)[, 2]) +``` +Please note that there are different ways to derive the contrasts. +The following code shows the implementation of some of these ways but it is not executed and the contrast specification above is used. +```{r , eval = FALSE} +# i) the frequentist contrast +contr_mat_prior <- getContr( + mods = mods, + dose_levels = dose_levels, + dose_weights = n_patients, + prior_list = prior_list) +# ii) re-estimated frequentist contrasts +contr_mat_prior <- getContr( + mods = mods, + dose_levels = dose_levels, + se_new_trial = new_trial$se) +# iii) Bayesian approach using number of patients for new trial and prior distribution +contr_mat_prior <- getContr( + mods = mods, + dose_levels = dose_levels, + dose_weights = n_patients, + prior_list = prior_list) +``` +The Bayesian MCP testing step is then executed based on the posterior information, the provided contrasts and the multiplicity adjusted critical value. +```{r Execution of Bayesian MCPMod Test step} +BMCP_result <- performBayesianMCP( + posterior_list = posterior, + contr = contr_mat, + crit_prob_adj = crit_pval) + +BMCP_result +``` +The testing step is significant indicating a non-flat dose-response shape. +In detail, all 3 models are significant and the p-value for the emax model indicates deviation from the null hypothesis the most. + +# Model fitting and visualization of results + +In the model fitting step the posterior distribution is used as basis. +Both simplified and full fitting are performed. +For the simplified fit, the multivariate normal distribution of the control group is approximated and reduced by a one-dimensional normal distribution. +The actual fit (on this approximated posterior distribution) is then performed using generalized least squares criterion. +In contrast, for the full fit, the non-linear optimization problem is addressed via the Nelder Mead algorithm. + +The output of the fit includes information about the predicted effects for the included dose levels, the generalized AIC, and the corresponding weights. +For the considered case, the simplified and the full fit are very similar. +```{r Model fitting} +# Option a) Simplified approach by using approximated posterior distribution +fit_simple <- getModelFits( + models = names(mods), + dose_levels = dose_levels, + posterior = posterior, + simple = TRUE) + +# Option b) Making use of the complete posterior distribution +fit <- getModelFits( + models = names(mods), + dose_levels = dose_levels, + posterior = posterior, + simple = FALSE) +``` +Via the predict() function, one can also receive estimates for dose levels that were not included in the trial. +```{r Predict} +predict(fit, doses = c(0, 2.5, 4, 5, 7, 10)) +``` +It is possible to plot the fitted dose response models and an AIC based average model (black lines). +```{r Plot simple vs fit} +plot(fit_simple) +plot(fit) +``` + +To assess the uncertainty, one can additionally visualize credible bands (orange shaded areas, default levels are 50% and 95%). +These credible bands are calculated with a bootstrap method as follows: +Samples from the posterior distribution are drawn and for every sample the simplified fitting step and a prediction is performed. +These predictions are then used to identify and visualize the specified quantiles. +```{r Plot with bootstrap} +plot(fit, cr_bands = TRUE) +``` + +The bootstrap based quantiles can also be directly calculated via the getBootstrapQuantiles() function. +For this example, only 6 quantiles are bootstrapped for each model fit. +```{r Bootstrap} +bs_sample <- getBootstrapSamples( + model_fits = fit, + doses = c(0, 2.5, 4, 5, 7, 10), + n_samples = 6) + +getBootstrapQuantiles( + bs_sample = bs_sample, + quantiles = c(0.025, 0.5, 0.975)) +``` +Technical note: The median quantile of the bootstrap based procedure is not necessary similar to the main model fit, as they are derived via different procedures. +The main fit, i.e. the black lines in the plot, show the best fit of a certain model based on minimizing the residuals for the posterior distribution, while the bootstrap based 50% quantile shows the median fit of the random sampling and fitting procedure. + +# Additional note +It is also possible to perform the testing and modeling step in a combined fashion via the performBayesianMCPMod() function. +This code serves merely as an example and is not run in this vignette. +```{r, eval = FALSE} +performBayesianMCPMod( + posterior_list = posterior, + contr = contr_mat, + crit_prob_adj = crit_pval, + simple = FALSE) +``` From 537d579f3db3e30afe8767634f4a2496195c92a1 Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 11:11:39 +0200 Subject: [PATCH 37/49] minor change on DESCRIPTION file --- DESCRIPTION | 1 - 1 file changed, 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index b577081..737cffe 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -54,4 +54,3 @@ Language: en-US LazyData: true Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.1 -Language: en-US From 110fedfdd7f59d7372373fc8843290a15a20a1c0 Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 11:22:42 +0200 Subject: [PATCH 38/49] refactor --- _pkgdown.yml | 1 + .../{analysis_normal.qmd => analysis_normal_noninformative.qmd} | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) rename vignettes/{analysis_normal.qmd => analysis_normal_noninformative.qmd} (99%) diff --git a/_pkgdown.yml b/_pkgdown.yml index 1b0d774..934cedf 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -12,4 +12,5 @@ articles: navbar: ~ contents: - analysis_normal + - analysis_normal_noninformative - Simulation_Example diff --git a/vignettes/analysis_normal.qmd b/vignettes/analysis_normal_noninformative.qmd similarity index 99% rename from vignettes/analysis_normal.qmd rename to vignettes/analysis_normal_noninformative.qmd index 6124a67..9895648 100644 --- a/vignettes/analysis_normal.qmd +++ b/vignettes/analysis_normal_noninformative.qmd @@ -1,6 +1,5 @@ --- title: "Analysis Example of Bayesian MCPMod for Continuous Data" -subtitle: "WORK IN PROGRESS" date: today format: html: From acf33602e50dc5c7e4e5d6e9d3cfd5b78aeb949e Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 11:29:43 +0200 Subject: [PATCH 39/49] adjust and add quarto in gh actions --- .github/workflows/pkgdown.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pkgdown.yml b/.github/workflows/pkgdown.yml index e7d01f1..1fa6370 100644 --- a/.github/workflows/pkgdown.yml +++ b/.github/workflows/pkgdown.yml @@ -20,6 +20,7 @@ jobs: env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} steps: + - uses: quarto-dev/quarto-actions/setup@v2 - uses: actions/checkout@v2 - uses: r-lib/actions/setup-pandoc@v2 @@ -27,7 +28,7 @@ jobs: - uses: r-lib/actions/setup-r@v2 with: use-public-rspm: true - + - name: Install JAGS run: | sudo apt-get update -y From 408e9321ee5416f411ef4c1b047310bd79ecf01e Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 11:36:33 +0200 Subject: [PATCH 40/49] adjusted gh actions --- .github/workflows/linux.yml | 1 + .github/workflows/macos.yml | 1 + .github/workflows/windows.yml | 1 + 3 files changed, 3 insertions(+) diff --git a/.github/workflows/linux.yml b/.github/workflows/linux.yml index 8163e3c..8c2bc42 100644 --- a/.github/workflows/linux.yml +++ b/.github/workflows/linux.yml @@ -24,6 +24,7 @@ jobs: R_KEEP_PKG_SOURCE: yes steps: + - uses: quarto-dev/quarto-actions/setup@v2 - uses: actions/checkout@v2 - uses: r-lib/actions/setup-pandoc@v2 diff --git a/.github/workflows/macos.yml b/.github/workflows/macos.yml index 2b4c2c9..a4a3309 100644 --- a/.github/workflows/macos.yml +++ b/.github/workflows/macos.yml @@ -26,6 +26,7 @@ jobs: R_KEEP_PKG_SOURCE: yes steps: + - uses: quarto-dev/quarto-actions/setup@v2 - uses: actions/checkout@v2 - uses: r-lib/actions/setup-pandoc@v2 diff --git a/.github/workflows/windows.yml b/.github/workflows/windows.yml index 68ff44d..cdfc20d 100644 --- a/.github/workflows/windows.yml +++ b/.github/workflows/windows.yml @@ -25,6 +25,7 @@ jobs: R_KEEP_PKG_SOURCE: yes steps: + - uses: quarto-dev/quarto-actions/setup@v2 - uses: actions/checkout@v2 - uses: r-lib/actions/setup-pandoc@v2 From 73228fad2b2c70d675b58dffc148c7eeeb62024e Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 12:29:23 +0200 Subject: [PATCH 41/49] fixing minor things --- R/bootstrapping.R | 95 ++++++------ R/modeling.R | 122 +++++++-------- R/s3methods.R | 154 +++++++++---------- vignettes/analysis_normal_noninformative.qmd | 1 - 4 files changed, 186 insertions(+), 186 deletions(-) diff --git a/R/bootstrapping.R b/R/bootstrapping.R index 444ed60..f80801e 100644 --- a/R/bootstrapping.R +++ b/R/bootstrapping.R @@ -1,12 +1,12 @@ #' @title getBootstrapSamples #' -#' @description A function for the calculation of bootstrapped model predictions. -#' Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. -#' These fits are then used to identify the specified quantiles. +#' @description A function for the calculation of bootstrapped model predictions. +#' Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. +#' These fits are then used to identify the specified quantiles. #' This approach can be considered as the Bayesian equivalent of the frequentist bootstrap approach described in O'Quigley et al. (2017). #' Instead of drawing n bootstrap samples from the sampling distribution of the trial dose-response estimates, here the samples are directly taken from the posterior distribution. #' @references O'Quigley J, Iasonos A, Bornkamp B. 2017. Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (1st ed.). Chapman and Hall/CRC. doi:10.1201/9781315151984 -#' @param model_fits An object of class modelFits, i.e. information about fitted models & corresponding model coefficients as well as the posterior distribution that was the basis for the model fitting +#' @param model_fits An object of class modelFits, i.e. information about fitted models & corresponding model coefficients as well as the posterior distribution that was the basis for the model fitting #' @param n_samples Number of samples that should be drawn as basis for the bootstrapped quantiles #' @param doses A vector of doses for which a prediction should be performed #' @param avg_fit Boolean variable, defining whether an average fit (based on generalized AIC weights) should be performed in addition to the individual models. Default TRUE. @@ -14,7 +14,7 @@ #' @examples #' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), #' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), -#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , +#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , #' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , #' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) #' models <- c("exponential", "linear") @@ -23,40 +23,40 @@ #' posterior = posterior_list, #' dose_levels = dose_levels, #' simple = TRUE) -#' +#' #' getBootstrapSamples(model_fits = fit, #' n_samples = 10, # speeding up example run time #' doses = c(0, 6, 8)) -#' -#' @return A data frame with columns for model, dose, and bootstrapped samples -#' +#' +#' @return A data frame with columns for model, dose, and bootstrapped samples +#' #' @export getBootstrapSamples <- function ( - + model_fits, n_samples = 1e3, doses = NULL, avg_fit = TRUE - + ) { - + mu_hat_samples <- sapply(attr(model_fits, "posterior"), RBesT::rmix, n = n_samples) sd_hat <- summary.postList(attr(model_fits, "posterior"))[, 2] - + dose_levels <- model_fits[[1]]$dose_levels model_names <- names(model_fits) - + if (is.null(doses)) { - + doses <- seq(min(dose_levels), max(dose_levels), length.out = 100L) - + } - + preds <- apply(mu_hat_samples, 1, function (mu_hat) { - + preds_mu_hat <- sapply(model_names, function (model) { - + fit <- DoseFinding::fitMod( dose = model_fits[[1]]$dose_levels, resp = mu_hat, @@ -65,47 +65,48 @@ getBootstrapSamples <- function ( type = "general", bnds = DoseFinding::defBnds( mD = max(model_fits[[1]]$dose_levels))[[model]]) - + preds <- stats::predict(fit, doseSeq = doses, predType = "ls-means") attr(preds, "gAIC") <- DoseFinding::gAIC(fit) - + return (preds) - + }, simplify = FALSE) - + preds_mu_mat <- do.call(rbind, preds_mu_hat) - + if (avg_fit) { - + avg_fit_indx <- which.min(sapply(preds_mu_hat, attr, "gAIC")) preds_mu_mat <- rbind(preds_mu_mat, avgFit = preds_mu_mat[avg_fit_indx, ]) - + } - + return (preds_mu_mat) - + }) - + if (avg_fit) { - + model_names <- c(model_names, "avgFit") - + } - + bs_samples <- as.data.frame(preds[order(rep(seq_along(model_names), length(doses))), ]) colnames(bs_samples) <- paste0("preds_", seq_len(n_samples)) - + bs_samples_data <- cbind( doses = doses, models = rep( x = factor(model_names, levels = model_names), - each = length(doses)), - as.data.frame(quant_mat)) - + each = length(doses)) + ) + # as.data.frame(preds)) + # tidyr::pivot_longer(bs_samples_data, cols = contains("preds")) - + return (bs_samples_data) - + } #' @title getBootstrapQuantiles @@ -113,12 +114,12 @@ getBootstrapSamples <- function ( #' @description Calculates quantiles from bootstrapped dose predictions. #' Can be used to derive credible intervals to assess the uncertainty for the model fit. #' @param bs_samples An object of class bootstrappedSample as created by getBootstrapSamples -#' @param quantiles A vector of quantiles that should be evaluated +#' @param quantiles A vector of quantiles that should be evaluated #' #' @examples #' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), #' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), -#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , +#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , #' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , #' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) #' models <- c("exponential", "linear") @@ -127,15 +128,15 @@ getBootstrapSamples <- function ( #' posterior = posterior_list, #' dose_levels = dose_levels, #' simple = TRUE) -#' +#' #' bs_samples <- getBootstrapSamples(model_fits = fit, #' n_samples = 10, # speeding up example run time #' doses = c(0, 6, 8)) -#' +#' #' getBootstrapQuantiles(bs_samples = bs_samples, #' quantiles = c(0.025, 0.5, 0.975)) -#' @return A data frame with entries doses, models, and quantiles -#' +#' @return A data frame with entries doses, models, and quantiles +#' #' @export getBootstrapQuantiles <- function ( @@ -143,14 +144,14 @@ getBootstrapQuantiles <- function ( quantiles ) { - + quantile_probs <- sort(unique(quantiles)) - + bs_quantiles <- t(apply(X = bs_samples[, -c(1, 2)], MARGIN = 1, FUN = stats::quantile, probs = quantile_probs)) - + bs_quantiles_data <- cbind( bs_samples[, c(1, 2)], as.data.frame(bs_quantiles)) diff --git a/R/modeling.R b/R/modeling.R index 132b596..1d52eca 100644 --- a/R/modeling.R +++ b/R/modeling.R @@ -1,12 +1,12 @@ #' @title getModelFits -#' +#' #' @description Fits dose-response curves for the specified dose-response models, based on the posterior distributions. -#' For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions. -#' For the default case, the Nelder-Mead algorithm is used. +#' For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions. +#' For the default case, the Nelder-Mead algorithm is used. #' In detail, for both approaches the mean vector \eqn{\theta^{Y}} and the covariance \eqn{\Sigma} of the (mixture) posterior distributions and the corresponding posterior weights \eqn{\tilde{\omega}_{l}} for \eqn{l \in {1,...,L}} are used as basis #' For the full fit a GLS estimator is used to minimize the following expression for the respective dose-response models \eqn{m} #' \deqn{ \hat{\theta}_{m}=argmin_{\theta_{m}} \sum_{l=1}^{L} \tilde{\omega}_{l}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))'\Sigma_{l}^{-1}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))} -#' Therefore the function nloptr of the nloptr package is utilized. +#' Therefore the function nloptr of the nloptr package is utilized. #' In the simplified case \eqn{L=1}, as the dimension of the posterior is reduced to 1 first. #' The generalized AIC values are calculated via the formula #' \deqn{gAIC_{m} = \sum_{l=1}^{L} \tilde{\omega}_{l} \sum_{i=0}^{K} \frac{1}{\Sigma_{l_{i,i}}} (\theta_{l_i}^Y - f(dose_{i},\hat{\theta}_{m}))^2 + 2p } @@ -20,17 +20,17 @@ #' @references Schorning K, Bornkamp B, Bretz F, Dette H. 2016. Model selection versus model averaging in dose finding studies. Stat Med; 35; 4021-4040. #' @param models List of model names for which a fit will be performed. #' @param dose_levels A vector containing the different dosage levels. -#' @param posterior A getPosterior object, containing the (multivariate) posterior distribution per dosage level. +#' @param posterior A getPosterior object, containing the (multivariate) posterior distribution per dosage level. #' @param simple Boolean variable, defining whether simplified fit will be applied. Default FALSE. #' @examples #' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), #' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), -#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , +#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , #' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , #' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) #' models <- c("emax", "exponential", "sigEmax", "linear") #' dose_levels <- c(0, 1, 2, 4, 8) -#' +#' #' fit <- getModelFits(models = models, #' posterior = posterior_list, #' dose_levels = dose_levels) @@ -38,17 +38,17 @@ #' posterior = posterior_list, #' dose_levels = dose_levels, #' simple = TRUE) -#' +#' #' @return An object of class modelFits. A list containing information about the fitted model coefficients, the prediction per dose group as well as maximum effect and generalized AIC (and corresponding weight) per model. -#' +#' #' @export getModelFits <- function ( - + models, dose_levels, posterior, simple = FALSE - + ) { if (inherits(models, "character")) { models <- stats::setNames(as.list(models), models) @@ -57,33 +57,33 @@ getModelFits <- function ( checkmate::check_double(dose_levels, lower = 0, any.missing = FALSE, len = length(models)) checkmate::check_class(posterior, "postList") checkmate::check_logical(simple) - + model_names <- unique(gsub("\\d", "", names(models))) getModelFit <- ifelse(simple, getModelFitSimple, getModelFitOpt) model_fits <- lapply(model_names, getModelFit, dose_levels, posterior, list("scal" = attr(models, "scal"))) model_fits <- addModelWeights(model_fits) - + names(model_fits) <- model_names attr(model_fits, "posterior") <- posterior class(model_fits) <- "modelFits" - - + + return (model_fits) - + } ## ignoring mixture posterior getModelFitSimple <- function ( - + model, dose_levels, posterior, addArgs = NULL - + ) { - + fit <- DoseFinding::fitMod( dose = dose_levels, resp = summary.postList(posterior)[, 1], @@ -91,11 +91,11 @@ getModelFitSimple <- function ( model = model, type = "general", bnds = DoseFinding::defBnds(mD = max(dose_levels))[[model]]) - + pred_vals <- stats::predict(fit, predType = "ls-means") max_effect <- max(pred_vals) - min(pred_vals) gAIC <- DoseFinding::gAIC(fit) - + model_fit <- list( model = model, coeffs = fit$coefs, @@ -103,28 +103,28 @@ getModelFitSimple <- function ( pred_values = pred_vals, max_effect = max_effect, gAIC = gAIC) - + return (model_fit) - + } ## taking mixture posterior into account getModelFitOpt <- function ( - + model, dose_levels, posterior, addArgs = NULL - + ) { - + getOptParams <- function ( - + model, dose_levels, posterior, addArgs = NULL - + ) { switch ( model, @@ -165,18 +165,18 @@ getModelFitOpt <- function ( expr_i <- substitute( sum( ((post_combs$means[i, ] - (theta[1] + theta[2] * (((theta[3] + theta[4])^(theta[3] + theta[4])) / ((theta[3] ^ theta[3]) * (theta[4]^theta[4]))) * (dose_levels / scal)^(theta[3]) * ((1 - dose_levels / scal)^(theta[4]))))^2 / (post_combs$vars[i, ]))) - , + , list(scal = scal) ) }, stop(paste("error:", model, "shape not yet implemented")) ) - + simple_fit <- getModelFitSimple( model = model, dose_levels = dose_levels, posterior = posterior) - + param_list <- list( expr_i = expr_i, opts = list("algorithm" = "NLOPT_LN_NELDERMEAD", maxeval = 1e3), #,stopval=0 @@ -185,22 +185,22 @@ getModelFitOpt <- function ( lb = lb, ub = ub), c_names = names(simple_fit$coeffs)) - + return (param_list) - + } - + optFun <- function ( theta, dose_levels, post_combs, expr_i ) { - + comps <- sapply(seq_along(post_combs$weights), function (i) eval(expr_i)) - + return (sum(post_combs$weights * comps)) - + } param_list <- getOptParams(model, dose_levels, posterior) @@ -216,28 +216,28 @@ getModelFitOpt <- function ( post_combs = post_combs, expr_i = param_list$expr_i ) - + names(fit$solution) <- param_list$c_names - + model_fit <- list( model = model, coeffs = fit$solution, dose_levels = dose_levels) - + model_fit$pred_values <- predictModelFit(model_fit = model_fit, doses = model_fit$dose_levels, addArgs = addArgs) model_fit$max_effect <- max(model_fit$pred_values) - min(model_fit$pred_values) model_fit$gAIC <- getGenAIC(model_fit, post_combs) - + return (model_fit) - + } predictModelFit <- function ( - + model_fit, doses = NULL, addArgs = NULL - + ) { if (is.null(doses)) { doses <- model_fit$dose_levels @@ -283,44 +283,44 @@ predictModelFit <- function ( model_fit$coeffs["delta2"], ifelse(is.null(addArgs) | is.null(addArgs[["scal"]]), 1.2 * max(doses), addArgs[["scal"]]))}, {stop(paste("error:", model_fit$model, "shape not yet implemented"))}) - + return (pred_vals) - + } getGenAIC <- function ( - + model_fit, post_combs - + ) { - + expr_i <- quote(sum(1 / post_combs$vars[i, ] * (post_combs$means[i, ] - model_fit$pred_values)^2) + 2 * length(model_fit$coeffs)) comb_aic <- sapply(seq_along(post_combs$weights), function (i) eval(expr_i)) - + g_aic <- stats::weighted.mean(comb_aic, w = post_combs$weights) - + return (g_aic) - + } addModelWeights <- function ( - + model_fits - + ) { g_aics <- sapply(model_fits, function (model_fit) model_fit$gAIC) exp_values <- exp(-0.5 * g_aics) model_weights <- exp_values / sum(exp_values) - + names(model_weights) <- NULL - + model_fits_out <- lapply(seq_along(model_fits), function (i) { - + c(model_fits[[i]], model_weight = model_weights[i]) - + }) - + return (model_fits_out) - -} \ No newline at end of file + +} diff --git a/R/s3methods.R b/R/s3methods.R index 39f9dc1..c714a0d 100644 --- a/R/s3methods.R +++ b/R/s3methods.R @@ -2,14 +2,14 @@ #' @export print.BayesianMCPMod <- function ( - + x, ... - + ) { - + print(x$BayesianMCP) - + } ## BayesianMCP -------------------------------------------- @@ -17,16 +17,16 @@ print.BayesianMCPMod <- function ( #' @export print.BayesianMCP <- function(x, ...) { cat("Bayesian Multiple Comparison Procedure\n\n") - + cat("Summary:\n") cat(" Sign:", x[1, "sign"], "\n") cat(" Critical Probability:", x[1, "crit_prob_adj"], "\n") cat(" Maximum Posterior Probability:", x[1, "max_post_prob"], "\n\n") - + n_sim <- nrow(x) cat("Effective Sample Size (ESS) per Dose Group:\n") print(attr(x, "ess_avg"), row.names = FALSE) cat("\n") - + cat("Posterior Probabilities for Model Shapes:\n") model_probs <- x[1, grep("^post_probs\\.", colnames(x))] model_names <- gsub("post_probs\\.", "", names(model_probs)) @@ -34,63 +34,63 @@ print.BayesianMCP <- function(x, ...) { print(model_df, row.names = FALSE) cat("Bayesian Multiple Comparison Procedure\n") - + if (n_sim == 1L) { - + attr(x, "critProbAdj") <- NULL attr(x, "successRate") <- NULL class(x) <- NULL - + print.default(x, ...) - + # if (any(!is.na(attr(x, "essAvg")))) { - # + # # cat("Average Posterior ESS\n") # print(attr(x, "essAvg"), ...) - # + # # } - + } else { - + model_successes <- getModelSuccesses(x) - + cat(" Estimated Success Rate: ", attr(x, "successRate"), "\n") cat(" N Simulations: ", n_sim) - + cat("\n") cat("Model Significance Frequencies\n") print(model_successes, ...) - + } - + invisible(x) } # print.BayesianMCP <- function ( - # + # # x, # ... -# +# # ) { -# +# # n_sim <- nrow(x) -# +# # cat("Bayesian Multiple Comparison Procedure\n") -# +# # if (n_sim == 1L) { -# +# # attr(x, "crit_prob_adj") <- NULL # attr(x, "success_rate") <- NULL # class(x) <- NULL -# +# # print.default(x, ...) -# +# # } else { -# +# # cat(" Estimated Success Rate: ", attr(x, "successRate"), "\n") # cat(" N Simulations: ", n_sim) -# +# # } -# +# # } @@ -98,17 +98,17 @@ print.BayesianMCP <- function(x, ...) { #' @title predict.modelFits #' @description This function performs model predictions based on the provided -#' model and dose specifications -#' +#' model and dose specifications +#' #' @param object A modelFits object containing information about the fitted #' model coefficients #' @param doses A vector specifying the doses for which a prediction should be -#' done +#' done #' @param ... Currently without function #' @examples #' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), #' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), -#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , +#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , #' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , #' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) #' models <- c("emax", "exponential", "sigEmax", "linear") @@ -116,56 +116,56 @@ print.BayesianMCP <- function(x, ...) { #' fit <- getModelFits(models = models, #' posterior = posterior_list, #' dose_levels = dose_levels) -#' +#' #' predict(fit, doses = c(0, 1, 3, 4, 6, 8)) -#' +#' #' @return a list with the model predictions for the specified models and doses -#' +#' #' @export predict.modelFits <- function ( - + object, doses = NULL, ... - + ) { - + predictions <- lapply(object, predictModelFit, doses = doses) attr(predictions, "doses") <- doses - + return (predictions) - + } #' @export print.modelFits <- function ( - + x, ... - + ) { - + n_digits = 1 - + dose_levels <- x[[1]]$dose_levels dose_names <- names(attr(x, "posterior")) - + predictions <- t(sapply(x, function (y) y$pred_values)) colnames(predictions) <- dose_names - + out_table <- data.frame(predictions, mEff = sapply(x, function (y) y$max_effect), gAIC = sapply(x, function (y) y$gAIC), w = sapply(x, function (y) y$model_weight)) out_table <- apply(as.matrix(out_table), 2, round, digits = n_digits) - + if (!is.null(x[[1]]$significant)) { - + out_table <- as.data.frame(out_table) out_table$Sign <- sapply(x, function (y) y$significant) - + } - + model_names <- names(x) |> gsub("exponential", "exponential", x = _) |> gsub("quadratic", "quadratic ", x = _) |> @@ -173,18 +173,18 @@ print.modelFits <- function ( gsub("logistic", "logistic ", x = _) |> gsub("emax", "emax ", x = _) |> gsub("sigEmax", "sigEmax ", x = _) - + # cat("Bayesian MCP Model Fits\n\n") cat("Model Coefficients\n") for (i in seq_along(model_names)) { - + coeff_values <- x[[i]]$coeff coeff_names <- names(coeff_values) - + cat(model_names[i], paste(coeff_names, round(coeff_values, n_digits), sep = " = "), "\n", sep = " ") - + } cat("\n") cat("Dose Levels\n", @@ -192,7 +192,7 @@ print.modelFits <- function ( cat("\n") cat("Predictions, Maximum Effect, gAIC, Model Weights & Significance\n") print(out_table, ...) - + } ## plot.ModelFits() @@ -202,57 +202,57 @@ print.modelFits <- function ( #' @export summary.postList <- function ( - + object, ... - + ) { - + summary_list <- lapply(object, summary, ...) names(summary_list) <- names(object) summary_tab <- do.call(rbind, summary_list) - + return (summary_tab) - + } #' @export print.postList <- function ( - + x, ... - + ) { - + getMaxDiff <- function ( - + medians - + ) { - + diffs <- medians - medians[1] - + max_diff <- max(diffs) max_diff_level <- which.max(diffs) - 1 - + out <- c(max_diff, max_diff_level) names(out) <- c("max_diff", "DG") - + return (out) - + } - + summary_tab <- summary.postList(x) - + names(x) <- rownames(summary_tab) class(x) <- NULL - + list_out <- list(summary_tab, getMaxDiff(summary_tab[, 4]), x) names(list_out) <- c("Summary of Posterior Distributions", "Maximum Difference to Control and Dose Group", "Posterior Distributions") - + print(list_out, ...) - + } diff --git a/vignettes/analysis_normal_noninformative.qmd b/vignettes/analysis_normal_noninformative.qmd index 9895648..9097042 100644 --- a/vignettes/analysis_normal_noninformative.qmd +++ b/vignettes/analysis_normal_noninformative.qmd @@ -82,7 +82,6 @@ display_params_table <- function(named_list) { } } -library(BayesianMCPMod) library(RBesT) library(clinDR) library(dplyr) From a078450e2409a5f0a1a9e146bef8af748e70b657 Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 13:48:14 +0200 Subject: [PATCH 42/49] adjustment based on function changes --- NAMESPACE | 1 - R/bootstrapping.R | 68 ++++++++++---------- R/plot.R | 113 ++++++++++++++++------------------ man/getBootstrapQuantiles.Rd | 45 +++++++++----- man/getBootstrapSamples.Rd | 48 --------------- man/getModelFits.Rd | 4 +- man/plot.modelFits.Rd | 6 +- man/predict.modelFits.Rd | 4 +- vignettes/analysis_normal.Rmd | 10 ++- 9 files changed, 133 insertions(+), 166 deletions(-) delete mode 100644 man/getBootstrapSamples.Rd diff --git a/NAMESPACE b/NAMESPACE index 499ea56..dc6a5e4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,7 +9,6 @@ S3method(print,postList) S3method(summary,postList) export(assessDesign) export(getBootstrapQuantiles) -export(getBootstrapSamples) export(getContr) export(getCritProb) export(getESS) diff --git a/R/bootstrapping.R b/R/bootstrapping.R index f80801e..b3e5699 100644 --- a/R/bootstrapping.R +++ b/R/bootstrapping.R @@ -6,7 +6,9 @@ #' This approach can be considered as the Bayesian equivalent of the frequentist bootstrap approach described in O'Quigley et al. (2017). #' Instead of drawing n bootstrap samples from the sampling distribution of the trial dose-response estimates, here the samples are directly taken from the posterior distribution. #' @references O'Quigley J, Iasonos A, Bornkamp B. 2017. Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (1st ed.). Chapman and Hall/CRC. doi:10.1201/9781315151984 +#' #' @param model_fits An object of class modelFits, i.e. information about fitted models & corresponding model coefficients as well as the posterior distribution that was the basis for the model fitting +#' @param quantiles A vector of quantiles that should be evaluated #' @param n_samples Number of samples that should be drawn as basis for the bootstrapped quantiles #' @param doses A vector of doses for which a prediction should be performed #' @param avg_fit Boolean variable, defining whether an average fit (based on generalized AIC weights) should be performed in addition to the individual models. Default TRUE. @@ -25,15 +27,17 @@ #' simple = TRUE) #' #' getBootstrapSamples(model_fits = fit, +#' quantiles = c(0.025, 0.5, 0.975), #' n_samples = 10, # speeding up example run time #' doses = c(0, 6, 8)) #' #' @return A data frame with columns for model, dose, and bootstrapped samples #' #' @export -getBootstrapSamples <- function ( +getBootstrapQuantiles <- function ( model_fits, + quantiles, n_samples = 1e3, doses = NULL, avg_fit = TRUE @@ -46,6 +50,7 @@ getBootstrapSamples <- function ( dose_levels <- model_fits[[1]]$dose_levels model_names <- names(model_fits) + quantile_probs <- sort(unique(quantiles)) if (is.null(doses)) { @@ -92,20 +97,21 @@ getBootstrapSamples <- function ( } - bs_samples <- as.data.frame(preds[order(rep(seq_along(model_names), length(doses))), ]) - colnames(bs_samples) <- paste0("preds_", seq_len(n_samples)) + sort_indx <- order(rep(seq_along(model_names), length(doses))) + quant_mat <- t(apply(X = preds[sort_indx, ], + MARGIN = 1, + FUN = stats::quantile, + probs = quantile_probs)) + - bs_samples_data <- cbind( + cr_bounds_data <- cbind( doses = doses, models = rep( x = factor(model_names, levels = model_names), - each = length(doses)) - ) - # as.data.frame(preds)) + each = length(doses)), + as.data.frame(quant_mat)) - # tidyr::pivot_longer(bs_samples_data, cols = contains("preds")) - - return (bs_samples_data) + return (cr_bounds_data) } @@ -138,26 +144,26 @@ getBootstrapSamples <- function ( #' @return A data frame with entries doses, models, and quantiles #' #' @export -getBootstrapQuantiles <- function ( - - bs_samples, - quantiles - -) { - - quantile_probs <- sort(unique(quantiles)) - - bs_quantiles <- t(apply(X = bs_samples[, -c(1, 2)], - MARGIN = 1, - FUN = stats::quantile, - probs = quantile_probs)) - - bs_quantiles_data <- cbind( - bs_samples[, c(1, 2)], - as.data.frame(bs_quantiles)) - - return (bs_quantiles_data) - -} +# getBootstrapQuantiles <- function ( +# +# bs_samples, +# quantiles +# +# ) { +# +# quantile_probs <- sort(unique(quantiles)) +# +# bs_quantiles <- t(apply(X = bs_samples[, -c(1, 2)], +# MARGIN = 1, +# FUN = stats::quantile, +# probs = quantile_probs)) +# +# bs_quantiles_data <- cbind( +# bs_samples[, c(1, 2)], +# as.data.frame(bs_quantiles)) +# +# return (bs_quantiles_data) +# +# } diff --git a/R/plot.R b/R/plot.R index 3633dac..bd2d9d8 100644 --- a/R/plot.R +++ b/R/plot.R @@ -1,11 +1,11 @@ #' @title plot.modelFits -#' +#' #' @description Plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit. #' Black lines show the fitted dose response models and an AIC based average model. -#' Dots indicate the posterior median and vertical lines show corresponding credible intervals (i.e. the variability of the posterior distribution of the respective dose group). +#' Dots indicate the posterior median and vertical lines show corresponding credible intervals (i.e. the variability of the posterior distribution of the respective dose group). #' To assess the uncertainty of the model fit one can in addition visualize credible bands (default coloring as orange shaded areas). #' The calculation of these bands is performed via the getBootstrapQuantiles() function. -#' The default setting is that these credible bands are not calculated. +#' The default setting is that these credible bands are not calculated. #' @param x An object of type modelFits #' @param gAIC Logical value indicating whether gAIC values are shown in the plot. Default TRUE #' @param avg_fit Logical value indicating whether average fit is presented in the plot. Default TRUE @@ -19,7 +19,7 @@ #' @examples #' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), #' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), -#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , +#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , #' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , #' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) #' models <- c("exponential", "linear") @@ -28,12 +28,12 @@ #' posterior = posterior_list, #' dose_levels = dose_levels, #' simple = TRUE) -#' -#' plot(fit) +#' +#' plot(fit) #' @return A ggplot2 object #' @export plot.modelFits <- function ( - + x, gAIC = TRUE, avg_fit = TRUE, @@ -44,12 +44,11 @@ plot.modelFits <- function ( n_bs_smpl = 1e3, acc_color = "orange", ... - + ) { - ## R CMD --as-cran appeasement .data <- NULL - + checkmate::check_class(x, "modelFits") checkmate::check_logical(gAIC) checkmate::check_logical(avg_fit) @@ -59,10 +58,10 @@ plot.modelFits <- function ( checkmate::check_double(alpha_CrB, lower = 0, upper = 1, len = 2) checkmate::check_integer(n_bs_smpl, lower = 1, upper = Inf) checkmate::check_string(acc_color, na.ok = TRUE) - + plot_res <- 1e2 model_fits <- x - + dose_levels <- model_fits[[1]]$dose_levels post_summary <- summary.postList( object = attr(model_fits, "posterior"), @@ -70,33 +69,33 @@ plot.modelFits <- function ( dose_seq <- seq(from = min(dose_levels), to = max(dose_levels), length.out = plot_res) - + preds_models <- sapply(model_fits, predictModelFit, doses = dose_seq) model_names <- names(model_fits) - + if (avg_fit) { - + mod_weights <- sapply(model_fits, function (x) x$model_weight) avg_preds <- preds_models %*% mod_weights - + preds_models <- cbind(preds_models, avg_preds) model_names <- c(model_names, "avgFit") - + } - + gg_data <- data.frame( doses = rep(dose_seq, length(model_names)), fits = as.vector(preds_models), models = rep(factor(model_names, levels = model_names), each = plot_res)) - + if (gAIC) { - + g_AICs <- sapply(model_fits, function (x) x$gAIC) label_gAUC <- paste("AIC:", round(g_AICs, digits = 1)) - + if (avg_fit) { - + mod_weights <- sort(mod_weights, decreasing = TRUE) paste_names <- names(mod_weights) |> gsub("exponential", "exp", x = _) |> @@ -106,45 +105,41 @@ plot.modelFits <- function ( gsub("sigEmax", "sigE", x = _) |> gsub("betaMod", "betaM", x = _)|> gsub("quadratic", "quad", x = _) - + label_avg <- paste0(paste_names, "=", round(mod_weights, 1), collapse = ", ") label_gAUC <- c(label_gAUC, label_avg) - + } - + } - + if (cr_bands) { - - bs_samples <- getBootstrapSamples( + + crB_data <- getBootstrapQuantiles( model_fits = model_fits, n_samples = n_bs_smpl, + quantiles = c(0.5, sort(unique(c(alpha_CrB / 2, 1 - alpha_CrB / 2)))), avg_fit = avg_fit, doses = dose_seq) - - crB_data <- getBootstrapQuantiles( - bs_samples = bs_samples, - quantiles = c(0.5, sort(unique(c(alpha_CrB / 2, 1 - alpha_CrB / 2))))) - colnames(crB_data) <- c("models", colnames(crB_data)[-1]) - + getInx <- function (alpha_CrB) { n <- length(alpha_CrB) - inx_list <- lapply(seq_len(n), function (i) c(i, 2 * n - i + 2) + 2) + inx_list <- lapply(seq_len(n), function (i) c(i, 2 * n - i + 1) + 3) return (inx_list)} - + } - - plts <- ggplot2::ggplot() + + + plts <- ggplot2::ggplot() + ## Layout etc. - ggplot2::theme_bw() + + ggplot2::theme_bw() + ggplot2::labs(x = "Dose", y = "Model Fits") + ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank()) + ## gAIC {if (gAIC) { - + ggplot2::geom_text( data = data.frame( models = unique(gg_data$models), @@ -152,42 +147,42 @@ plot.modelFits <- function ( mapping = ggplot2::aes(label = label_gAUC), x = -Inf, y = Inf, hjust = "inward", vjust = "inward", size = 3) - + } } - + if (cr_bands) { - + ## Bootstrapped Credible Bands for (inx in getInx(alpha_CrB)) { - + loop_txt <- paste0( "ggplot2::geom_ribbon( data = crB_data, - mapping = ggplot2::aes(x = .data$dose, + mapping = ggplot2::aes(x = doses, ymin = crB_data[, ", inx[1], "], ymax = crB_data[, ", inx[2], "]), - fill = acc_color, + fill = acc_color, alpha = 0.25)") - + plts <- plts + eval(parse(text = loop_txt)) - + } rm(inx) - + ## Bootstrapped Fit plts <- plts + ggplot2::geom_line( data = crB_data, - mapping = ggplot2::aes(.data$dose, .data$`50%`), + mapping = ggplot2::aes(.data$doses, .data$`50%`), color = acc_color) - + } - + plts <- plts + ## Posterior Credible Intervals {if (cr_intv) { - + ggplot2::geom_errorbar( data = data.frame(x = dose_levels, ymin = post_summary[, 3], @@ -197,9 +192,9 @@ plot.modelFits <- function ( ymax = .data$ymax), width = 0, alpha = 0.5) - + } - } + + } + ## Posterior Medians ggplot2::geom_point( data = data.frame( @@ -210,10 +205,10 @@ plot.modelFits <- function ( ## Fitted Models ggplot2::geom_line( data = gg_data, - mapping = ggplot2::aes(.data$doses, .data$fits)) + + mapping = ggplot2::aes(.data$doses, .data$fits)) + ## Faceting ggplot2::facet_wrap(~ models) - + return (plts) - -} \ No newline at end of file + +} diff --git a/man/getBootstrapQuantiles.Rd b/man/getBootstrapQuantiles.Rd index 82d5889..f5e5bd7 100644 --- a/man/getBootstrapQuantiles.Rd +++ b/man/getBootstrapQuantiles.Rd @@ -2,26 +2,41 @@ % Please edit documentation in R/bootstrapping.R \name{getBootstrapQuantiles} \alias{getBootstrapQuantiles} -\title{getBootstrapQuantiles} +\title{getBootstrapSamples} \usage{ -getBootstrapQuantiles(bs_samples, quantiles) +getBootstrapQuantiles( + model_fits, + quantiles, + n_samples = 1000, + doses = NULL, + avg_fit = TRUE +) } \arguments{ -\item{bs_samples}{An object of class bootstrappedSample as created by getBootstrapSamples} +\item{model_fits}{An object of class modelFits, i.e. information about fitted models & corresponding model coefficients as well as the posterior distribution that was the basis for the model fitting} \item{quantiles}{A vector of quantiles that should be evaluated} + +\item{n_samples}{Number of samples that should be drawn as basis for the bootstrapped quantiles} + +\item{doses}{A vector of doses for which a prediction should be performed} + +\item{avg_fit}{Boolean variable, defining whether an average fit (based on generalized AIC weights) should be performed in addition to the individual models. Default TRUE.} } \value{ -A data frame with entries doses, models, and quantiles +A data frame with columns for model, dose, and bootstrapped samples } \description{ -Calculates quantiles from bootstrapped dose predictions. -Can be used to derive credible intervals to assess the uncertainty for the model fit. +A function for the calculation of bootstrapped model predictions. +Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. +These fits are then used to identify the specified quantiles. +This approach can be considered as the Bayesian equivalent of the frequentist bootstrap approach described in O'Quigley et al. (2017). +Instead of drawing n bootstrap samples from the sampling distribution of the trial dose-response estimates, here the samples are directly taken from the posterior distribution. } \examples{ posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), - DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , + DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) models <- c("exponential", "linear") @@ -30,11 +45,13 @@ fit <- getModelFits(models = models, posterior = posterior_list, dose_levels = dose_levels, simple = TRUE) - -bs_samples <- getBootstrapSamples(model_fits = fit, - n_samples = 10, # speeding up example run time - doses = c(0, 6, 8)) - -getBootstrapQuantiles(bs_samples = bs_samples, - quantiles = c(0.025, 0.5, 0.975)) + +getBootstrapSamples(model_fits = fit, + quantiles = c(0.025, 0.5, 0.975), + n_samples = 10, # speeding up example run time + doses = c(0, 6, 8)) + +} +\references{ +O'Quigley J, Iasonos A, Bornkamp B. 2017. Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (1st ed.). Chapman and Hall/CRC. doi:10.1201/9781315151984 } diff --git a/man/getBootstrapSamples.Rd b/man/getBootstrapSamples.Rd deleted file mode 100644 index e81068e..0000000 --- a/man/getBootstrapSamples.Rd +++ /dev/null @@ -1,48 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/bootstrapping.R -\name{getBootstrapSamples} -\alias{getBootstrapSamples} -\title{getBootstrapSamples} -\usage{ -getBootstrapSamples(model_fits, n_samples = 1000, doses = NULL, avg_fit = TRUE) -} -\arguments{ -\item{model_fits}{An object of class modelFits, i.e. information about fitted models & corresponding model coefficients as well as the posterior distribution that was the basis for the model fitting} - -\item{n_samples}{Number of samples that should be drawn as basis for the bootstrapped quantiles} - -\item{doses}{A vector of doses for which a prediction should be performed} - -\item{avg_fit}{Boolean variable, defining whether an average fit (based on generalized AIC weights) should be performed in addition to the individual models. Default TRUE.} -} -\value{ -A data frame with columns for model, dose, and bootstrapped samples -} -\description{ -A function for the calculation of bootstrapped model predictions. -Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. -These fits are then used to identify the specified quantiles. -This approach can be considered as the Bayesian equivalent of the frequentist bootstrap approach described in O'Quigley et al. (2017). -Instead of drawing n bootstrap samples from the sampling distribution of the trial dose-response estimates, here the samples are directly taken from the posterior distribution. -} -\examples{ -posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), - DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), - DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , - DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , - DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) -models <- c("exponential", "linear") -dose_levels <- c(0, 1, 2, 4, 8) -fit <- getModelFits(models = models, - posterior = posterior_list, - dose_levels = dose_levels, - simple = TRUE) - -getBootstrapSamples(model_fits = fit, - n_samples = 10, # speeding up example run time - doses = c(0, 6, 8)) - -} -\references{ -O'Quigley J, Iasonos A, Bornkamp B. 2017. Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (1st ed.). Chapman and Hall/CRC. doi:10.1201/9781315151984 -} diff --git a/man/getModelFits.Rd b/man/getModelFits.Rd index 8e2023d..0f37483 100644 --- a/man/getModelFits.Rd +++ b/man/getModelFits.Rd @@ -40,7 +40,7 @@ where \eqn{Q} denotes the number of models included in the averaging procedure. \examples{ posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), - DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , + DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) models <- c("emax", "exponential", "sigEmax", "linear") @@ -53,7 +53,7 @@ fit_simple <- getModelFits(models = models, posterior = posterior_list, dose_levels = dose_levels, simple = TRUE) - + } \references{ Schorning K, Bornkamp B, Bretz F, Dette H. 2016. Model selection versus model averaging in dose finding studies. Stat Med; 35; 4021-4040. diff --git a/man/plot.modelFits.Rd b/man/plot.modelFits.Rd index cbd372a..53e9256 100644 --- a/man/plot.modelFits.Rd +++ b/man/plot.modelFits.Rd @@ -52,7 +52,7 @@ The default setting is that these credible bands are not calculated. \examples{ posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), - DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , + DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) models <- c("exponential", "linear") @@ -61,6 +61,6 @@ fit <- getModelFits(models = models, posterior = posterior_list, dose_levels = dose_levels, simple = TRUE) - -plot(fit) + +plot(fit) } diff --git a/man/predict.modelFits.Rd b/man/predict.modelFits.Rd index b819293..fc3c835 100644 --- a/man/predict.modelFits.Rd +++ b/man/predict.modelFits.Rd @@ -25,7 +25,7 @@ model and dose specifications \examples{ posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), - DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , + DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) models <- c("emax", "exponential", "sigEmax", "linear") @@ -33,7 +33,7 @@ dose_levels <- c(0, 1, 2, 4, 8) fit <- getModelFits(models = models, posterior = posterior_list, dose_levels = dose_levels) - + predict(fit, doses = c(0, 1, 3, 4, 6, 8)) } diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd index af6159f..2424264 100644 --- a/vignettes/analysis_normal.Rmd +++ b/vignettes/analysis_normal.Rmd @@ -264,14 +264,12 @@ plot(fit, cr_bands = TRUE) The bootstrap based quantiles can also be directly calculated via the getBootstrapQuantiles() function. For this example, only 6 quantiles are bootstrapped for each model fit. ```{r Bootstrap} -bs_sample <- getBootstrapSamples( +BayesianMCPMod::getBootstrapQuantiles( model_fits = fit, + quantiles = c(0.025, 0.5, 0.975), doses = c(0, 2.5, 4, 5, 7, 10), - n_samples = 6) - -getBootstrapQuantiles( - bs_sample = bs_sample, - quantiles = c(0.025, 0.5, 0.975)) + n_samples = 6 +) ``` Technical note: The median quantile of the bootstrap based procedure is not necessary similar to the main model fit, as they are derived via different procedures. The main fit, i.e. the black lines in the plot, show the best fit of a certain model based on minimizing the residuals for the posterior distribution, while the bootstrap based 50% quantile shows the median fit of the random sampling and fitting procedure. From 830ef948b331ab792c033b1522e68917b4d7e84a Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 14:00:06 +0200 Subject: [PATCH 43/49] bug fix --- R/bootstrapping.R | 54 ++--------------------------- man/getBootstrapQuantiles.Rd | 4 +-- tests/testthat/test-bootstrapping.R | 41 +++++++++++----------- 3 files changed, 24 insertions(+), 75 deletions(-) diff --git a/R/bootstrapping.R b/R/bootstrapping.R index b3e5699..4a28b7e 100644 --- a/R/bootstrapping.R +++ b/R/bootstrapping.R @@ -1,4 +1,4 @@ -#' @title getBootstrapSamples +#' @title getBootstrapQuantiles #' #' @description A function for the calculation of bootstrapped model predictions. #' Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. @@ -26,7 +26,7 @@ #' dose_levels = dose_levels, #' simple = TRUE) #' -#' getBootstrapSamples(model_fits = fit, +#' getBootstrapQuantiles(model_fits = fit, #' quantiles = c(0.025, 0.5, 0.975), #' n_samples = 10, # speeding up example run time #' doses = c(0, 6, 8)) @@ -115,55 +115,5 @@ getBootstrapQuantiles <- function ( } -#' @title getBootstrapQuantiles -#' -#' @description Calculates quantiles from bootstrapped dose predictions. -#' Can be used to derive credible intervals to assess the uncertainty for the model fit. -#' @param bs_samples An object of class bootstrappedSample as created by getBootstrapSamples -#' @param quantiles A vector of quantiles that should be evaluated -#' -#' @examples -#' posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2), -#' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2), -#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) , -#' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) , -#' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2)) -#' models <- c("exponential", "linear") -#' dose_levels <- c(0, 1, 2, 4, 8) -#' fit <- getModelFits(models = models, -#' posterior = posterior_list, -#' dose_levels = dose_levels, -#' simple = TRUE) -#' -#' bs_samples <- getBootstrapSamples(model_fits = fit, -#' n_samples = 10, # speeding up example run time -#' doses = c(0, 6, 8)) -#' -#' getBootstrapQuantiles(bs_samples = bs_samples, -#' quantiles = c(0.025, 0.5, 0.975)) -#' @return A data frame with entries doses, models, and quantiles -#' -#' @export -# getBootstrapQuantiles <- function ( -# -# bs_samples, -# quantiles -# -# ) { -# -# quantile_probs <- sort(unique(quantiles)) -# -# bs_quantiles <- t(apply(X = bs_samples[, -c(1, 2)], -# MARGIN = 1, -# FUN = stats::quantile, -# probs = quantile_probs)) -# -# bs_quantiles_data <- cbind( -# bs_samples[, c(1, 2)], -# as.data.frame(bs_quantiles)) -# -# return (bs_quantiles_data) -# -# } diff --git a/man/getBootstrapQuantiles.Rd b/man/getBootstrapQuantiles.Rd index f5e5bd7..4e22634 100644 --- a/man/getBootstrapQuantiles.Rd +++ b/man/getBootstrapQuantiles.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/bootstrapping.R \name{getBootstrapQuantiles} \alias{getBootstrapQuantiles} -\title{getBootstrapSamples} +\title{getBootstrapQuantiles} \usage{ getBootstrapQuantiles( model_fits, @@ -46,7 +46,7 @@ fit <- getModelFits(models = models, dose_levels = dose_levels, simple = TRUE) -getBootstrapSamples(model_fits = fit, +getBootstrapQuantiles(model_fits = fit, quantiles = c(0.025, 0.5, 0.975), n_samples = 10, # speeding up example run time doses = c(0, 6, 8)) diff --git a/tests/testthat/test-bootstrapping.R b/tests/testthat/test-bootstrapping.R index 7ad8ae8..a6a2a7e 100644 --- a/tests/testthat/test-bootstrapping.R +++ b/tests/testthat/test-bootstrapping.R @@ -1,37 +1,36 @@ # Test cases test_that("test getBootstrapBands", { - + BMCP_result <- performBayesianMCP( posterior_list = posterior_list, contr = contr_mat, crit_prob_adj = crit_pval) model_shapes <- c("linear", "emax", "exponential") - + # Option b) Making use of the complete posterior distribution fit <- getModelFits( models = model_shapes, dose_levels = dose_levels, posterior = posterior_list, simple = FALSE) - - bs_samples <- getBootstrapSamples(model_fits = fit, - doses = c(0, 1,2,3,4,6,8)) - - result <- getBootstrapQuantiles(bs_samples, quantiles = c(0.025,0.5, 0.975)) + + result <- bootstrap_quantiles <- BayesianMCPMod::getBootstrapQuantiles( + model_fits = fit, + quantiles = c(0.025, 0.5, 0.975), + doses = c(0, 2.5, 4, 5, 7, 10), + n_samples = 6 + ) + expect_type(result, "list") - - bs_samples_2 <- getBootstrapSamples(model_fits = fit, - n_samples = 1e2, - avg_fit = FALSE, - doses = c(1, 2, 3)) - - result_2 <- getBootstrapQuantiles(bs_samples_2, quantiles = c(0.1, 0.9)) - expect_type(result_2, "list") - - bs_samples_3 <- getBootstrapSamples(model_fits = fit, - doses = NULL) - result_3 <- getBootstrapQuantiles(bs_samples_3, quantiles = c(0.025,0.5, 0.975)) - expect_type(result_3, "list") -}) \ No newline at end of file + result_2 <- bootstrap_quantiles <- BayesianMCPMod::getBootstrapQuantiles( + model_fits = fit, + quantiles = c(0.025, 0.5, 0.975), + avg_fit = FALSE, + doses = c(0, 2.5, 4, 5, 7, 10), + n_samples = 6 + ) + + expect_type(result_2, "list") +}) From 46a6e20bc7cf46bb0de6e6be1cd6dab626f1e83d Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 14:06:06 +0200 Subject: [PATCH 44/49] removed explicit calls --- vignettes/analysis_normal.Rmd | 2 +- vignettes/analysis_normal_noninformative.qmd | 20 ++++++++++---------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd index 2424264..d5d2323 100644 --- a/vignettes/analysis_normal.Rmd +++ b/vignettes/analysis_normal.Rmd @@ -264,7 +264,7 @@ plot(fit, cr_bands = TRUE) The bootstrap based quantiles can also be directly calculated via the getBootstrapQuantiles() function. For this example, only 6 quantiles are bootstrapped for each model fit. ```{r Bootstrap} -BayesianMCPMod::getBootstrapQuantiles( +getBootstrapQuantiles( model_fits = fit, quantiles = c(0.025, 0.5, 0.975), doses = c(0, 2.5, 4, 5, 7, 10), diff --git a/vignettes/analysis_normal_noninformative.qmd b/vignettes/analysis_normal_noninformative.qmd index 9097042..da97567 100644 --- a/vignettes/analysis_normal_noninformative.qmd +++ b/vignettes/analysis_normal_noninformative.qmd @@ -225,7 +225,7 @@ In the first step of Bayesian MCPMod, the posterior is calculated by combining the prior information with the estimated results of the trial [@fleischer_2022]. ```{r} -posterior <- BayesianMCPMod::getPosterior( +posterior <- getPosterior( prior_list = prior_list, mu_hat = trial_data$rslt, se_hat = trial_data$se, @@ -247,14 +247,14 @@ A pseudo-optimal contrast matrix is generated based on the variability of the posterior distribution (see [@fleischer_2022] for more details). ```{r} -crit_pval <- BayesianMCPMod::getCritProb( +crit_pval <- getCritProb( mods = mods, dose_levels = dose_levels, se_new_trial = trial_data$se, alpha_crit_val = 0.05 ) -contr_mat <- BayesianMCPMod::getContr( +contr_mat <- getContr( mods = mods, dose_levels = dose_levels, sd_posterior = summary(posterior)[, 2] @@ -269,18 +269,18 @@ executed and the contrast specification above is used. #| eval: false # i) the frequentist contrast -contr_mat_prior <- BayesianMCPMod::getContr( +contr_mat_prior <- getContr( mods = mods, dose_levels = dose_levels, dose_weights = n_patients, prior_list = prior_list) # ii) re-estimated frequentist contrasts -contr_mat_prior <- BayesianMCPMod::getContr( +contr_mat_prior <- getContr( mods = mods, dose_levels = dose_levels, se_new_trial = trial_data$se) # iii) Bayesian approach using number of patients for new trial and prior distribution -contr_mat_prior <- BayesianMCPMod::getContr( +contr_mat_prior <- getContr( mods = mods, dose_levels = dose_levels, dose_weights = n_patients, @@ -290,7 +290,7 @@ contr_mat_prior <- BayesianMCPMod::getContr( The Bayesian MCP testing step is then executed: ```{r} -BMCP_result <- BayesianMCPMod::performBayesianMCP( +BMCP_result <- performBayesianMCP( posterior_list = posterior, contr = contr_mat, crit_prob_adj = crit_pval) @@ -329,7 +329,7 @@ present the full fit. ```{r} # If simple = TRUE, uses approx posterior # Here we use complete posterior distribution -fit <- BayesianMCPMod::getModelFits( +fit <- getModelFits( models = mods, dose_levels = dose_levels, posterior = posterior, @@ -368,7 +368,7 @@ The bootstrap based quantiles can also be directly calculated via the For this example, only 6 quantiles are bootstrapped for each model fit. ```{r} -bootstrap_quantiles <- BayesianMCPMod::getBootstrapQuantiles( +bootstrap_quantiles <- getBootstrapQuantiles( model_fits = fit, quantiles = c(0.025, 0.5, 0.975), doses = c(0, 2.5, 4, 5, 7, 10), @@ -403,7 +403,7 @@ but this is not run here. ```{r} #| eval: false -BayesianMCPMod::performBayesianMCPMod( +performBayesianMCPMod( posterior_list = posterior, contr = contr_mat, crit_prob_adj = crit_pval, From 2c8390e2617360658f78c797cbed2316cb0131da Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 14:09:50 +0200 Subject: [PATCH 45/49] minor --- vignettes/analysis_normal_noninformative.qmd | 1 + 1 file changed, 1 insertion(+) diff --git a/vignettes/analysis_normal_noninformative.qmd b/vignettes/analysis_normal_noninformative.qmd index da97567..98f0431 100644 --- a/vignettes/analysis_normal_noninformative.qmd +++ b/vignettes/analysis_normal_noninformative.qmd @@ -82,6 +82,7 @@ display_params_table <- function(named_list) { } } +library(BayesianMCPMod) library(RBesT) library(clinDR) library(dplyr) From 0420d1860e18a89fbe8ed782f15a44b7a2f9efc0 Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 14:21:51 +0200 Subject: [PATCH 46/49] rebuild --- DESCRIPTION | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 737cffe..485b55b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -6,8 +6,9 @@ Authors@R: c( person("Boehringer Ingelheim Pharma GmbH & Co. KG", role = c("cph", "fnd")), person("Stephan", "Wojciekowski", , "stephan.wojciekowski@boehringer-ingelheim.com", role = c("aut", "cre")), person("Lars", "Andersen", , "lars.andersen@boehringer-ingelheim.com", role = "aut"), - person("Steven", "Brooks", , "steven.brooks@boehringer-ingelheim.com", role = "ctb"), - person("Sebastian", "Bossert", , "sebastian.bossert@boehringer-ingelheim.com", role = "aut") + person("Sebastian", "Bossert", , "sebastian.bossert@boehringer-ingelheim.com", role = "aut"), + person("Steven", "Brooks", , "steven.brooks@boehringer-ingelheim.com", role = "ctb") + ) Description: Bayesian MCPMod (Fleischer et al. (2022) ) is an innovative method that improves the From 58b535b315a3721bebb99f13f83d14920d33dfef Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 14:25:23 +0200 Subject: [PATCH 47/49] minor --- DESCRIPTION | 1 - 1 file changed, 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 485b55b..c9ddc6b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -8,7 +8,6 @@ Authors@R: c( person("Lars", "Andersen", , "lars.andersen@boehringer-ingelheim.com", role = "aut"), person("Sebastian", "Bossert", , "sebastian.bossert@boehringer-ingelheim.com", role = "aut"), person("Steven", "Brooks", , "steven.brooks@boehringer-ingelheim.com", role = "ctb") - ) Description: Bayesian MCPMod (Fleischer et al. (2022) ) is an innovative method that improves the From 7fa55d313bdbc39a7370f7118d8277bf5023e3a0 Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 15:18:40 +0200 Subject: [PATCH 48/49] further adjustments --- .Rbuildignore | 1 + vignettes/.gitignore | 3 ++- vignettes/analysis_normal_noninformative.qmd | 21 ++++++++++---------- 3 files changed, 14 insertions(+), 11 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 9eaae49..bda7f19 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -11,3 +11,4 @@ docs ^cran-comments\.md$ ^cran_submission_script\.R$ ^CRAN-SUBMISSION$ +^vignettes/*_files$ diff --git a/vignettes/.gitignore b/vignettes/.gitignore index 5d528d8..5d2fa41 100644 --- a/vignettes/.gitignore +++ b/vignettes/.gitignore @@ -2,4 +2,5 @@ *.R *_cache/* *_files/* -*archive \ No newline at end of file +*archive +*_files diff --git a/vignettes/analysis_normal_noninformative.qmd b/vignettes/analysis_normal_noninformative.qmd index 98f0431..f1b5659 100644 --- a/vignettes/analysis_normal_noninformative.qmd +++ b/vignettes/analysis_normal_noninformative.qmd @@ -1,6 +1,5 @@ --- title: "Analysis Example of Bayesian MCPMod for Continuous Data" -date: today format: html: fig-height: 3.5 @@ -14,6 +13,17 @@ vignette: > %\VignetteEngine{quarto::html} --- +```{r setup} +library(BayesianMCPMod) +library(RBesT) +library(clinDR) +library(dplyr) +library(tibble) +library(reactable) + +set.seed(7015) +``` + ```{r} #| code-summary: setup #| code-fold: true @@ -81,15 +91,6 @@ display_params_table <- function(named_list) { ) } } - -library(BayesianMCPMod) -library(RBesT) -library(clinDR) -library(dplyr) -library(tibble) -library(reactable) - -set.seed(7015) ``` # Introduction From 97b937d0c16a778f50cb50837b5e733a1a935ec2 Mon Sep 17 00:00:00 2001 From: Andersen Date: Fri, 28 Jun 2024 15:31:30 +0200 Subject: [PATCH 49/49] explicit install on workflow added --- .github/workflows/windows.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/windows.yml b/.github/workflows/windows.yml index cdfc20d..fe07329 100644 --- a/.github/workflows/windows.yml +++ b/.github/workflows/windows.yml @@ -41,6 +41,10 @@ jobs: extra-packages: any::rcmdcheck needs: check + - name: Install BayesianMCPMod + shell: bash + run: R CMD INSTALL --preclean . + - uses: r-lib/actions/check-r-package@v2 with: upload-snapshots: true