Nothing
R/assessDesign.R
In BayesianMCPMod: Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod
#' @title assessDesign
#'
#' @description This function performs simulation based trial design evaluations for a set of specified dose-response models
#'
#' @param n_patients Vector specifying the planned number of patients per dose group. A minimum of 2 patients are required in each group.
#' @param mods An object of class `Mods` as specified in the `DoseFinding` package.
#' @param prior_list A prior_list object specifying the utilized prior for the different dose groups
#' @param sd A positive value, specification of assumed sd. Not required if either `data_sim` or `estimates_sim` is provided. Also not required in case of binary endpoint. Default NULL
#' @param contr An object of class `optContr` as created by the `DoseFinding::getContr` function. Allows specification of a fixed contrasts matrix. Default NULL.
#' @param dr_means A vector, allows specification of individual (not model based) assumed effects per dose group. Default NULL.
#' @param data_sim An optional data frame for custom simulated data. Must follow the data structure as provided by `simulateData()`. Default NULL.
#' @param estimates_sim An optional named list of 1) list of vectors for the estimated means per dose group (`estimates_sim$mu_hats`) and 2) of list of matrices for the covariance matrices specifying the (estimated) variabilities (`estimates_sim$S_hats`). Dimensions of entries must match the number of dose levels. Default NULL.
#' @param n_sim Number of simulations to be performed
#' @param alpha_crit_val (Un-adjusted) Critical value to be used for the MCP testing step. Passed to the `getCritProb` function for the calculation of adjusted critical values (on the probability scale). Default 0.05.
#' @param modeling Boolean variable defining whether the Mod part of Bayesian MCP-Mod will be performed in the assessment. More heavy on resources. Default FALSE.
#' @param simple Boolean variable defining whether simplified fit will be applied, see `?getModelFits`. Set automatically to TRUE if argument `delta` is provided. Passed to `getModelFits` Default TRUE.
#' @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.
#' @param reestimate Boolean variable defining whether critical value should be calculated with re-estimated contrasts (see `getCritProb` function for more details). Default FALSE.
#' @param delta A numeric value for the threshold Delta for the MED assessment. If NULL, no MED assessment is performed. Default NULL.
#' @param evidence_level A numeric value between 0 and 1 for the evidence level gamma for the MED assessment. Only required for Bayesian MED assessment, see `?getMED` for details. If NULL, MED assessment will be performed on the fitted model according to the argument `med_selection`. Default NULL.
#' @param n_bs_samples Number of bootstrap samples for the MED assessment if `evidence_level` is provided. Note that more extreme quantiles (i.e., quantiles closer to 0 or 1) tend to require more bootstrap samples to maintain precision. Default 1000.
#' @param med_selection A string, either `"avgFit"` or `"bestFit"`, for the method of MED selection. Default `"avgFit"`.
#' @param probability_scale A boolean to specify if the trial has a continuous or a binary outcome. Setting to TRUE will transform calculations from the logit scale to the probability scale, which can be desirable for a binary outcome. Default FALSE.
#'
#' @return Returns success probabilities for the different assumed dose-response shapes, attributes also includes information around average success rate (across all assumed models) and prior Effective sample size.
#'
#' @examples
#' mods <- DoseFinding::Mods(linear = NULL,
#' emax = c(0.5, 1.2),
#' exponential = 2,
#' betaMod = c(1, 1),
#' doses = c(0, 0.5, 2,4, 8),
#' maxEff = 6)
#'
#' sd <- 12
#' prior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 12), sigma = 2),
#' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.2, s = 11), sigma = 2) ,
#' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.3, s = 11), sigma = 2) ,
#' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2))
#' n_patients <- c(40, 60, 60, 60, 60)
#' dose_levels <- c(0, 0.5, 2, 4, 8)
#'
#' success_probabilities <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' sd = sd,
#' n_sim = 1e2) # speed up example run time
#'
#' success_probabilities
#'
#' ## Analysis with custom data
#' data_sim <- simulateData(
#' n_patients = n_patients,
#' dose_levels = dose_levels,
#' sd = sd,
#' mods = mods,
#' n_sim = 10)
#'
#' success_probabilities_cd <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' data_sim = data_sim,
#' sd = sd,
#' n_sim = 1e2) # speed up example run time
#'
#' success_probabilities_cd
#'
#' ## Analysis with custom dose response relationship
#' custom_dr_means <- c(1, 2, 3, 4, 5)
#'
#' success_probs_custom_dr <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' dr_means = custom_dr_means,
#' sd = sd,
#' n_sim = 1e2) # speed up example run time
#'
#' success_probs_custom_dr
#'
#' ## Analysis with custom estimates for means and variabilies
#' ## No simulated data, only simulated model estimates
#' estimates_sim <- list(mu_hats = replicate(100, list(c(1, 2, 3, 4, 5) + rnorm(5, 0, 1))),
#' S_hats = list(diag(1, 5)))
#'
#' success_probs_custom_est <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' estimates_sim = estimates_sim)
#'
#' success_probs_custom_est
#'
#' if (interactive()) { # takes typically > 5 seconds
#'
#' # with MED estimation without bootstrapping
#' # see ?getMED for details
#'
#' success_probabilities <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' sd = sd,
#' modeling = TRUE,
#' n_sim = 10, # speed up example run time
#' delta = 7)
#'
#' success_probabilities
#'
#' # with MED estimation with bootstrapping
#'
#' success_probabilities <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' sd = sd,
#' modeling = TRUE,
#' n_sim = 10, # speed up example run time
#' delta = 7,
#' evidence_level = 0.8)
#'
#' success_probabilities
#'
#' }
#'
#' @export
assessDesign <- function (
n_patients,
mods,
prior_list,
sd = NULL,
contr = NULL,
dr_means = NULL,
data_sim = NULL,
estimates_sim = NULL,
n_sim = 1e3,
alpha_crit_val = 0.05,
modeling = FALSE,
simple = TRUE,
avg_fit = TRUE,
reestimate = FALSE,
delta = NULL,
evidence_level = NULL,
n_bs_samples = 1000,
med_selection = c("avgFit", "bestFit"),
probability_scale = FALSE
) {
checkmate::assert_vector(n_patients, len = length(attr(mods, "doses")), any.missing = FALSE)
checkmate::assert_double(n_patients, lower = 2, upper = Inf)
checkmate::assert_class(mods, classes = "Mods")
checkmate::assert_list(prior_list, names = "named", len = length(attr(mods, "doses")), any.missing = FALSE)
checkmate::assert_list(estimates_sim, null.ok = TRUE)
# sensitive to how DoseFinding labels their attributes for "Mods" class
checkmate::assert_integerish(n_sim, lower = 1, upper = Inf)
checkmate::assert_double(alpha_crit_val, lower = 0, upper = 1)
checkmate::assert_flag(modeling)
checkmate::assert_flag(probability_scale)
# TODO: check that prior_list has 'sd_tot' attribute, and that it's numeric # this is not applicable at the moment
stopifnot("Cannot specify both 'data_sim' and 'estimates_sim' simultaneously" =
is.null(data_sim) & is.null(estimates_sim) |
!is.null(data_sim) & is.null(estimates_sim) |
is.null(data_sim) & !is.null(estimates_sim))
modeling <- ifelse(!is.null(delta), TRUE, modeling)
dose_levels <- attr(mods, "doses")
if (!is.null(estimates_sim)) {
stopifnot("'estimates_sim' must be a named list of lenght 2 with list items called 'mu_hats' and 'S_hats'" =
length(estimates_sim) == 2L &
all(names(estimates_sim) %in% c("mu_hats", "S_hats")))
stopifnot("The length of 'estimates_sim$S_hats' must match 'estimates_sim$mu_hats' or 1." =
length(estimates_sim$S_hats) == length(estimates_sim$mu_hats) |
length(estimates_sim$S_hats) == 1L)
stopifnot("The length of each entry of 'estimates_sim$mu_hats' must match the number of dose levels." =
length(estimates_sim$mu_hats[[1]]) == length(dose_levels))
stopifnot("The dimensions of each entry of 'estimates_sim$S_hats' must match the number of dose levels." =
ncol(estimates_sim$S_hats[[1]]) == length(dose_levels))
} else if (!is.null(data_sim)) {
## lazily simulate data anyway ...
data <- simulateData(
n_patients = n_patients,
dose_levels = dose_levels,
sd = 1,
mods = mods,
n_sim = floor(nrow(data_sim) / sum(n_patients)), # adjust the number of simulations to data_sim
dr_means = dr_means,
probability_scale = probability_scale)
## ... and then check if formats of data_sim and data match
stopifnot("'data_sim' must follow the data structure as provided by simulateData()" =
isTRUE(all.equal(data_sim[, 1:3], data[, 1:3])))
stopifnot("data_sim must have named columns" = !is.null(colnames(data_sim)))
checkmate::assert_data_frame(data_sim, types = "numeric", any.missing = FALSE)
data <- data_sim
} else {
data <- simulateData(
n_patients = n_patients,
dose_levels = dose_levels,
sd = sd,
mods = mods,
n_sim = n_sim,
dr_means = dr_means,
probability_scale = probability_scale)
}
crit_prob_adj <- getCritProb(
mods = mods,
dose_levels = dose_levels,
dose_weights = n_patients,
alpha_crit_val = alpha_crit_val)
if (!reestimate & is.null(contr)) {
if (!is.null(data_sim) | !is.null(estimates_sim)) {
message("Consider to provide 'contr' for your custom simulated data or analysis results.")
}
contr <- getContr(
mods = mods,
dose_levels = dose_levels,
dose_weights = n_patients,
prior_list = prior_list)
}
# get model names of true underlying models
if (is.null(estimates_sim)) {
model_names <- colnames(data)[-c(1, 2, 3)]
} else {
model_names <- "estimates_sim"
}
eval_design <- lapply(model_names, function (model_name) {
if (is.null(estimates_sim)) {
posterior_list <- getPosterior(
data = getModelData(data, model_name),
prior_list = prior_list,
probability_scale = probability_scale)
} else {
posterior_list <- mapply(getPosterior,
mu_hat = estimates_sim$mu_hats,
S_hat = estimates_sim$S_hats,
MoreArgs = list(prior_list = prior_list),
SIMPLIFY = FALSE)
names(posterior_list) <- seq_along(estimates_sim$mu_hats)
}
if (reestimate & is.null(contr)) {
post_sds <- sapply(posterior_list, function (post) summary(post)[, 2])
contr <- apply(post_sds, 2, function (post_sd) getContr(
mods = mods,
dose_levels = dose_levels,
cov_posterior = diag(post_sd^2)))
}
if (modeling) {
attr(contr, "direction") <- attr(mods,"direction")
b_mcp_mod <- performBayesianMCPMod(
posterior_list = posterior_list,
contr = contr,
crit_prob_adj = crit_prob_adj,
simple = simple,
avg_fit = avg_fit,
delta = delta,
evidence_level = evidence_level,
med_selection = med_selection,
n_samples = n_bs_samples,
probability_scale = probability_scale) # should this also be transferred via attribute?
} else {
b_mcp <- performBayesianMCP(
posterior_list = posterior_list,
contr = contr,
crit_prob_adj = crit_prob_adj)
}
})
names(eval_design) <- model_names
avg_success_rate <- mean(sapply(eval_design, function (x) {
ifelse(identical(modeling, FALSE),
attr(x, "successRate"),
attr(x$BayesianMCP, "successRate"))
}))
attr(eval_design, "avgSuccessRate") <- avg_success_rate
if (modeling & !is.null(delta)) {
avg_med_id_rate <- mean(sapply(eval_design, function (x) {
mean(attr(x, "MED")[, "med_reached"])
}))
attr(eval_design, "avgMEDIdentificationRate") <- avg_med_id_rate
}
attr(eval_design, "placEff") <- ifelse(test = is.null(dr_means),
yes = attr(mods, "placEff"),
no = dr_means[1])
attr(eval_design, "maxEff") <- ifelse(test = is.null(dr_means),
yes = attr(mods, "maxEff"), # this is on logit scale in case of binary endpoint
no = diff(range(dr_means)))
attr(eval_design, "sampleSize") <- n_patients
attr(eval_design, "priorESS") <- round(getESS(prior_list), 1)
return (eval_design)
}
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#' @title assessDesign
#'
#' @description This function performs simulation based trial design evaluations for a set of specified dose-response models
#'
#' @param n_patients Vector specifying the planned number of patients per dose group. A minimum of 2 patients are required in each group.
#' @param mods An object of class `Mods` as specified in the `DoseFinding` package.
#' @param prior_list A prior_list object specifying the utilized prior for the different dose groups
#' @param sd A positive value, specification of assumed sd. Not required if either `data_sim` or `estimates_sim` is provided. Also not required in case of binary endpoint. Default NULL
#' @param contr An object of class `optContr` as created by the `DoseFinding::getContr` function. Allows specification of a fixed contrasts matrix. Default NULL.
#' @param dr_means A vector, allows specification of individual (not model based) assumed effects per dose group. Default NULL.
#' @param data_sim An optional data frame for custom simulated data. Must follow the data structure as provided by `simulateData()`. Default NULL.
#' @param estimates_sim An optional named list of 1) list of vectors for the estimated means per dose group (`estimates_sim$mu_hats`) and 2) of list of matrices for the covariance matrices specifying the (estimated) variabilities (`estimates_sim$S_hats`). Dimensions of entries must match the number of dose levels. Default NULL.
#' @param n_sim Number of simulations to be performed
#' @param alpha_crit_val (Un-adjusted) Critical value to be used for the MCP testing step. Passed to the `getCritProb` function for the calculation of adjusted critical values (on the probability scale). Default 0.05.
#' @param modeling Boolean variable defining whether the Mod part of Bayesian MCP-Mod will be performed in the assessment. More heavy on resources. Default FALSE.
#' @param simple Boolean variable defining whether simplified fit will be applied, see `?getModelFits`. Set automatically to TRUE if argument `delta` is provided. Passed to `getModelFits` Default TRUE.
#' @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.
#' @param reestimate Boolean variable defining whether critical value should be calculated with re-estimated contrasts (see `getCritProb` function for more details). Default FALSE.
#' @param delta A numeric value for the threshold Delta for the MED assessment. If NULL, no MED assessment is performed. Default NULL.
#' @param evidence_level A numeric value between 0 and 1 for the evidence level gamma for the MED assessment. Only required for Bayesian MED assessment, see `?getMED` for details. If NULL, MED assessment will be performed on the fitted model according to the argument `med_selection`. Default NULL.
#' @param n_bs_samples Number of bootstrap samples for the MED assessment if `evidence_level` is provided. Note that more extreme quantiles (i.e., quantiles closer to 0 or 1) tend to require more bootstrap samples to maintain precision. Default 1000.
#' @param med_selection A string, either `"avgFit"` or `"bestFit"`, for the method of MED selection. Default `"avgFit"`.
#' @param probability_scale A boolean to specify if the trial has a continuous or a binary outcome. Setting to TRUE will transform calculations from the logit scale to the probability scale, which can be desirable for a binary outcome. Default FALSE.
#'
#' @return Returns success probabilities for the different assumed dose-response shapes, attributes also includes information around average success rate (across all assumed models) and prior Effective sample size.
#'
#' @examples
#' mods <- DoseFinding::Mods(linear = NULL,
#' emax = c(0.5, 1.2),
#' exponential = 2,
#' betaMod = c(1, 1),
#' doses = c(0, 0.5, 2,4, 8),
#' maxEff = 6)
#'
#' sd <- 12
#' prior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 12), sigma = 2),
#' DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
#' DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.2, s = 11), sigma = 2) ,
#' DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.3, s = 11), sigma = 2) ,
#' DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2))
#' n_patients <- c(40, 60, 60, 60, 60)
#' dose_levels <- c(0, 0.5, 2, 4, 8)
#'
#' success_probabilities <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' sd = sd,
#' n_sim = 1e2) # speed up example run time
#'
#' success_probabilities
#'
#' ## Analysis with custom data
#' data_sim <- simulateData(
#' n_patients = n_patients,
#' dose_levels = dose_levels,
#' sd = sd,
#' mods = mods,
#' n_sim = 10)
#'
#' success_probabilities_cd <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' data_sim = data_sim,
#' sd = sd,
#' n_sim = 1e2) # speed up example run time
#'
#' success_probabilities_cd
#'
#' ## Analysis with custom dose response relationship
#' custom_dr_means <- c(1, 2, 3, 4, 5)
#'
#' success_probs_custom_dr <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' dr_means = custom_dr_means,
#' sd = sd,
#' n_sim = 1e2) # speed up example run time
#'
#' success_probs_custom_dr
#'
#' ## Analysis with custom estimates for means and variabilies
#' ## No simulated data, only simulated model estimates
#' estimates_sim <- list(mu_hats = replicate(100, list(c(1, 2, 3, 4, 5) + rnorm(5, 0, 1))),
#' S_hats = list(diag(1, 5)))
#'
#' success_probs_custom_est <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' estimates_sim = estimates_sim)
#'
#' success_probs_custom_est
#'
#' if (interactive()) { # takes typically > 5 seconds
#'
#' # with MED estimation without bootstrapping
#' # see ?getMED for details
#'
#' success_probabilities <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' sd = sd,
#' modeling = TRUE,
#' n_sim = 10, # speed up example run time
#' delta = 7)
#'
#' success_probabilities
#'
#' # with MED estimation with bootstrapping
#'
#' success_probabilities <- assessDesign(
#' n_patients = n_patients,
#' mods = mods,
#' prior_list = prior_list,
#' sd = sd,
#' modeling = TRUE,
#' n_sim = 10, # speed up example run time
#' delta = 7,
#' evidence_level = 0.8)
#'
#' success_probabilities
#'
#' }
#'
#' @export
assessDesign <- function (
n_patients,
mods,
prior_list,
sd = NULL,
contr = NULL,
dr_means = NULL,
data_sim = NULL,
estimates_sim = NULL,
n_sim = 1e3,
alpha_crit_val = 0.05,
modeling = FALSE,
simple = TRUE,
avg_fit = TRUE,
reestimate = FALSE,
delta = NULL,
evidence_level = NULL,
n_bs_samples = 1000,
med_selection = c("avgFit", "bestFit"),
probability_scale = FALSE
) {
checkmate::assert_vector(n_patients, len = length(attr(mods, "doses")), any.missing = FALSE)
checkmate::assert_double(n_patients, lower = 2, upper = Inf)
checkmate::assert_class(mods, classes = "Mods")
checkmate::assert_list(prior_list, names = "named", len = length(attr(mods, "doses")), any.missing = FALSE)
checkmate::assert_list(estimates_sim, null.ok = TRUE)
# sensitive to how DoseFinding labels their attributes for "Mods" class
checkmate::assert_integerish(n_sim, lower = 1, upper = Inf)
checkmate::assert_double(alpha_crit_val, lower = 0, upper = 1)
checkmate::assert_flag(modeling)
checkmate::assert_flag(probability_scale)
# TODO: check that prior_list has 'sd_tot' attribute, and that it's numeric # this is not applicable at the moment
stopifnot("Cannot specify both 'data_sim' and 'estimates_sim' simultaneously" =
is.null(data_sim) & is.null(estimates_sim) |
!is.null(data_sim) & is.null(estimates_sim) |
is.null(data_sim) & !is.null(estimates_sim))
modeling <- ifelse(!is.null(delta), TRUE, modeling)
dose_levels <- attr(mods, "doses")
if (!is.null(estimates_sim)) {
stopifnot("'estimates_sim' must be a named list of lenght 2 with list items called 'mu_hats' and 'S_hats'" =
length(estimates_sim) == 2L &
all(names(estimates_sim) %in% c("mu_hats", "S_hats")))
stopifnot("The length of 'estimates_sim$S_hats' must match 'estimates_sim$mu_hats' or 1." =
length(estimates_sim$S_hats) == length(estimates_sim$mu_hats) |
length(estimates_sim$S_hats) == 1L)
stopifnot("The length of each entry of 'estimates_sim$mu_hats' must match the number of dose levels." =
length(estimates_sim$mu_hats[[1]]) == length(dose_levels))
stopifnot("The dimensions of each entry of 'estimates_sim$S_hats' must match the number of dose levels." =
ncol(estimates_sim$S_hats[[1]]) == length(dose_levels))
} else if (!is.null(data_sim)) {
## lazily simulate data anyway ...
data <- simulateData(
n_patients = n_patients,
dose_levels = dose_levels,
sd = 1,
mods = mods,
n_sim = floor(nrow(data_sim) / sum(n_patients)), # adjust the number of simulations to data_sim
dr_means = dr_means,
probability_scale = probability_scale)
## ... and then check if formats of data_sim and data match
stopifnot("'data_sim' must follow the data structure as provided by simulateData()" =
isTRUE(all.equal(data_sim[, 1:3], data[, 1:3])))
stopifnot("data_sim must have named columns" = !is.null(colnames(data_sim)))
checkmate::assert_data_frame(data_sim, types = "numeric", any.missing = FALSE)
data <- data_sim
} else {
data <- simulateData(
n_patients = n_patients,
dose_levels = dose_levels,
sd = sd,
mods = mods,
n_sim = n_sim,
dr_means = dr_means,
probability_scale = probability_scale)
}
crit_prob_adj <- getCritProb(
mods = mods,
dose_levels = dose_levels,
dose_weights = n_patients,
alpha_crit_val = alpha_crit_val)
if (!reestimate & is.null(contr)) {
if (!is.null(data_sim) | !is.null(estimates_sim)) {
message("Consider to provide 'contr' for your custom simulated data or analysis results.")
}
contr <- getContr(
mods = mods,
dose_levels = dose_levels,
dose_weights = n_patients,
prior_list = prior_list)
}
# get model names of true underlying models
if (is.null(estimates_sim)) {
model_names <- colnames(data)[-c(1, 2, 3)]
} else {
model_names <- "estimates_sim"
}
eval_design <- lapply(model_names, function (model_name) {
if (is.null(estimates_sim)) {
posterior_list <- getPosterior(
data = getModelData(data, model_name),
prior_list = prior_list,
probability_scale = probability_scale)
} else {
posterior_list <- mapply(getPosterior,
mu_hat = estimates_sim$mu_hats,
S_hat = estimates_sim$S_hats,
MoreArgs = list(prior_list = prior_list),
SIMPLIFY = FALSE)
names(posterior_list) <- seq_along(estimates_sim$mu_hats)
}
if (reestimate & is.null(contr)) {
post_sds <- sapply(posterior_list, function (post) summary(post)[, 2])
contr <- apply(post_sds, 2, function (post_sd) getContr(
mods = mods,
dose_levels = dose_levels,
cov_posterior = diag(post_sd^2)))
}
if (modeling) {
attr(contr, "direction") <- attr(mods,"direction")
b_mcp_mod <- performBayesianMCPMod(
posterior_list = posterior_list,
contr = contr,
crit_prob_adj = crit_prob_adj,
simple = simple,
avg_fit = avg_fit,
delta = delta,
evidence_level = evidence_level,
med_selection = med_selection,
n_samples = n_bs_samples,
probability_scale = probability_scale) # should this also be transferred via attribute?
} else {
b_mcp <- performBayesianMCP(
posterior_list = posterior_list,
contr = contr,
crit_prob_adj = crit_prob_adj)
}
})
names(eval_design) <- model_names
avg_success_rate <- mean(sapply(eval_design, function (x) {
ifelse(identical(modeling, FALSE),
attr(x, "successRate"),
attr(x$BayesianMCP, "successRate"))
}))
attr(eval_design, "avgSuccessRate") <- avg_success_rate
if (modeling & !is.null(delta)) {
avg_med_id_rate <- mean(sapply(eval_design, function (x) {
mean(attr(x, "MED")[, "med_reached"])
}))
attr(eval_design, "avgMEDIdentificationRate") <- avg_med_id_rate
}
attr(eval_design, "placEff") <- ifelse(test = is.null(dr_means),
yes = attr(mods, "placEff"),
no = dr_means[1])
attr(eval_design, "maxEff") <- ifelse(test = is.null(dr_means),
yes = attr(mods, "maxEff"), # this is on logit scale in case of binary endpoint
no = diff(range(dr_means)))
attr(eval_design, "sampleSize") <- n_patients
attr(eval_design, "priorESS") <- round(getESS(prior_list), 1)
return (eval_design)
}
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