#' Generating function for Lognormal Data.
#'
#' \code{genlognormaldata()} function used mainly internally by
#' \code{lognormaltrialsimulator()} and \code{lognormaltrialsimulatornohist()} functions
#' to generate data for a two-arm clinical trial, experimental and control groups.
#' Can be used to generate random trial data.
#'
#' @param sample_size Number of subjects per arm.
#' @param mu1 meanlog parameter used in call to \code{rlnorm()}.
#' Used only in control arm.
#' @param mean_ratio Desired Mean Ratio between experimental and control groups.
#' @param common_sd sdlog parameter used in call to \code{rlnorm()}.
#' Used in both arms.
#' @param censor_value Value at which time-to-event data are right censored.
#'
#' @return \code{genlognormaldata()} returns a data frame with columns: 'id', 'treatment',
#' 'event_time', and 'status'.
#'
#' @examples
#' samplehistdata <- genlognormaldata(sample_size=60, mu1=1.06, mean_ratio=0.6,
#' common_sd=1.25, censor_value=3)
#' samplehistdata
#' @export
genlognormaldata <- function(sample_size, mu1, mean_ratio, common_sd, censor_value) {
# --------------------------------------------------------------- #
# The function genlognormaldata simulates a balanced clinical trial
# with 'sample_size' subjects per arm using a lognormal distribution.
# 'm1' is the lognormal location parameter, and 'common_sd' is the
# lognormal scale parameter for both arms. 'censor_value' is the
# value when right censoring occurs. As of 9/5/2016,
# genlognormaldata only generates data with right censoring.
# Random right censoring is not incorporated. 'mean_ratio is the
# ratio of group means (experimental group over control group).
#
# In the code below time1, mu1, test1, status1, etc. are data
# for the control goup.
# In the code below time2, mu2, test2, status2, etc. are data
# for the experimental group.
# --------------------------------------------------------------- #
# mu1 is the lognormal distribution mu parameter for the control group.
# The lognormal mean for control group is:
lnmean1 <- exp(mu1 + 0.5 * common_sd^2)
# given user specified mean_ratio, the lognormal mean for experimental
# group is:
lnmean2 <- mean_ratio * lnmean1
# Now, I need to transform the lognormal mean in the experimental group
# to the mu parameter for experimental group.
mu2 <- log(lnmean2) - 0.5 * common_sd^2
# Create event times for both groups
time1 <- stats::rlnorm(sample_size, meanlog = mu1, sdlog = common_sd)
time2 <- stats::rlnorm(sample_size, meanlog = mu2, sdlog = common_sd)
# Create variables needed for simulation when censor_value is specified.
if (!is.null(censor_value) == TRUE) {
test1 <- (time1 > censor_value) #Identify which times need right censoring.
test2 <- (time2 > censor_value)
status1 <- rep(1, sample_size) #Initialize the Status variable.
status2 <- rep(1, sample_size)
#For all observations that need to be right censored, set the time
# value to the right censor value and set the status value to 0
# (indicating right censoring). Status=1 implies observed event.
# Status=0 implies right censored event.
time1[test1] <- censor_value
time2[test2] <- censor_value
status1[test1] <- 0
status2[test2] <- 0
}
#Create status variable if censor_value is not specified (in such a case
# status = 1 for all observeations.)
if (is.null(censor_value) == TRUE) {
status1 <- rep(1, sample_size)
status2 <- rep(1, sample_size)
}
#Take all data created above and put into a data frame that contains
# the required variables.
subjid <- seq(from = 1, to = 2 * sample_size)
trt <- c(rep(0, sample_size), rep(1, sample_size))
time <- c(time1, time2)
status <- c(status1, status2)
gendata <- data.frame(subjid, trt, time, status)
colnames(gendata) <- c("id", "treatment", "event_time", "status")
return(gendata)
}
#' Log-likelihood function for two-arm trial with historical data using Lognormal
#' distribution.
#'
#' \code{lognormalloglike()} function only used internally by
#' \code{lognormaltrialsimulator()} function to estimate Lognormal model parameters
#' when clinical trial involves experimental and control groups as well as historical
#' control data. The lognormal log-likelihood is calculated by modeling \code{log(data)}
#' as a Gaussian random variable. Not to be called directly by user.
#'
#' @param params Three element vector of Lognormal parameters. Third element is log(sd),
#' where sd is a parameter required by dnorm(). The first and second elements
#' are the intercept (beta0) and treatment effect parameter (beta1), where the treatment effect is
#' a log mean ratio (experimental group over control group). The mu parameter required by
#' dnorm() is equal to params[1] + params[2]*treatment. It is assumed that the log(sd)
#' parameter is the same in both randomized and historical data. It is assumed that
#' the mu parameter in the randomized and historical control data is equal to params[1].
#' @param randdata Dataset of randomly generated trial data. Randomized trial datasets
#' must have 4 columns: id, treatment, event_time, and status. The value of treatment
#' must be 0 (control) or 1 (experimental). The values of event_time must be positive.
#' The values of status must be 0 (right censored event) or 1 (observed event).
#' @param histdata Dataset of historical data. Historical datasets must have 4 columns:
#' id, treatment, event_time, and status. The value of treatment should be 0. The
#' values of event_time must be positive. The values of status must be 0 (right
#' censored event) or 1 (observed event).
#' @param a0 Power prior parameter: 0 implies historical data is ignored and 1 implies
#' all information in historical data is used.
#'
#' @return \code{lognormalloglike()} returns a value of the loglikelihood function
#' given a set of Lognormal parameters, randomly generated trial data, and observed
#' historical data.
#'
#' @examples
#' #None
#' @keywords internal
#' @noRd
lognormalloglike <- function(params, randdata, histdata, a0) {
# --------------------------------------------------------------- #
# This function calculates the Lognormal log-likelihood given
# a vector of parameter values, a dataset of randomized trial
# data (two arms, no covariates beyond treatment), and a dataset
# of historical control data.
# The lognormal scale parameter is common in both randomized
# groups and the historical control group. The lognormal location
# parameter is assumed to be the same in both control groups.
# The location parameter for the randomized experimental group is
# a linear function of the control location and the treatment
# effect. The parameters are beta0, beta1, and logs. logs is the
# log of the common scale parameter. beta0 and beta1 are regression
# parameters that are linked to the Lognoraml location parameter,
# mu, via the identity link. beta1 is the log mean ratio
# (experimental group over control group), while beta0 is the
# location parameter for controls.
# --------------------------------------------------------------- #
# Get params. Note that the scale parameter is on the log scale.
# To calculate the lognormal standard deviation value one must use exp(logs).
beta0 <- params[1]
beta1 <- params[2]
logs <- params[3]
# Calculate the lognormal mu parameter vector for all randomized observations.
mu_i <- beta0 + beta1 * randdata$treatment
# Calculate the log-likelihood values for all randomized observations.
# Note that I am modeling the lognormal by taking the log of event_time and then
# using the Gaussian distribution for the log transformed values. Could have used
# lnorm(), but did it this way just because it is what I am used to doing.
ll_R <- randdata$status * stats::dnorm(log(randdata$event_time), mean = mu_i, sd = exp(logs), log = TRUE) + (1 - randdata$status) *
stats::pnorm(log(randdata$event_time), mean = mu_i, sd = exp(logs), log.p = TRUE, lower.tail = FALSE)
# Calculate the loglikelihood values for all historical control observations.
# Note that the lognormal mu parameter is equal to beta0.
ll_H <- histdata$status * stats::dnorm(log(histdata$event_time), mean = beta0, sd = exp(logs), log = TRUE) + (1 - histdata$status) *
stats::pnorm(log(histdata$event_time), mean = beta0, sd = exp(logs), log.p = TRUE, lower.tail = FALSE)
# Calculate the overall log likelihood by adding the randomized log-likelihood to the historical control
# log-likelihood by a0, where a0 is the power prior parameter. This a0 value is defined by the
# user and not estimated via object function optimization.
ll <- sum(ll_R) + a0 * sum(ll_H)
# Return the sum of all individual elements to the negative log-likelihood
return(-ll)
}
#' Log-likelihood function for two-arm trial with no historical data using Lognormal distribution.
#'
#' \code{lognormalloglikenohist()} function only used internally by
#' \code{lognormaltrialsimulatornohist()} function to estimate Lognormal model parameters
#' when clinical trial involves experimental and control groups but no historical control
#' data. The lognormal log-likelihood is calculated by modeling \code{log(data)} as
#' a Gaussian random variable. Not to be called directly by user.
#'
#' @param params Three element vector of Lognormal parameters. Third element is log(sd),
#' where sd is a parameter required by dnorm(). The first and second elements
#' are the intercept (beta0) and treatment effect parameter (beta1), where the treatment effect is
#' a log mean ratio (experimental group over control group). The mu parameter required by
#' dnorm() is equal to params[1] + params[2]*treatment. It is assumed that
#' the mu parameter in the randomized control data is equal to params[1].
#' @param randdata Dataset of randomly generated trial data. Randomized trial datasets
#' must have 4 columns: id, treatment, event_time, and status. The value of treatment
#' must be 0 (control) or 1 (experimental). The values of event_time must be positive.
#' The values of status must be 0 (right censored event) or 1 (observed event).
#'
#' @return \code{lognormalloglikenohist()} returns a value of the loglikelihood function
#' given a set of Lognormal parameters and randomly generated trial data.
#'
#' @examples
#' #None
#' @keywords internal
#' @noRd
lognormalloglikenohist <- function(params, randdata) {
# --------------------------------------------------------------- #
# This function calculates the Lognormal log-likelihood given
# a vector of parameter values, and a dataset of randomized trial
# data (two arms, no covariates beyond treatment). Historical
# data is not utilized in this log-likelihood function.
# The lognormal scale parameter is common in both randomized
# groups. The location parameter for the randomized experimental
# group is a linear function of the control location and the
# treatment effect. The parameters are beta0, beta1, and logs.
# logs is the log of the common scale parameter. beta0 and beta1
# are regression parameters that are linked to the Lognoraml
# location parameter, mu, via the identity link. beta1 is the
# log mean ratio (experimental group over control group), while
# beta0 is the location parameter for controls.
# --------------------------------------------------------------- #
# Get params. Note that the scale parameter is on the log scale.
# To calculate the lognormal standard deviation value one must use exp(logs).
beta0 <- params[1]
beta1 <- params[2]
logs <- params[3]
# Calculate the lognormal mu parameter vector for all randomized observations.
mu_i <- beta0 + beta1 * randdata$treatment
# Calculate the log-likelihood values for all randomized observations.
# Note that I am modeling the lognormal by taking the log of event_time and then
# using the Gaussian distribution for the log transformed values. Could have used
# lnorm(), but did it this way just because it is what I am used to doing.
ll_R <- randdata$status * stats::dnorm(log(randdata$event_time), mean = mu_i, sd = exp(logs), log = TRUE) + (1 - randdata$status) *
stats::pnorm(log(randdata$event_time), mean = mu_i, sd = exp(logs), log.p = TRUE, lower.tail = FALSE)
# Return the sum of all individual elements to the negative log-likelihood
return(sum(-ll_R))
}
#' Simulate a single randomized trial using a Lognormal outcome and information from
#' historical controls.
#'
#' \code{lognormaltrialsimulator()} function only used internally by
#' \code{lognormal_sim()} function to run a single trial simulation involving historical
#' control data and a Lognormal outcome.
#'
#' The simulation of a trial with a Lognormal outcome involving historical control data returns
#' an estimate of the mean ratio as well as an estimate of the log mean ratio variance.
#' Finally the simulation returns an indication of whether or not the simulated trial led to
#' a rejection of the null hypothesis (1) or not (0).
#'
#' \code{lognormaltrialsimulator()} should not be called directly by user.
#'
#' @param sample_size_val Number of subjects per arm.
#' @param histdata Dataset of historical data. Historical datasets must have 4 columns:
#' id, treatment, event_time, and status. The value of treatment should be 0. The
#' values of event_time must be positive. The values of status must be 0 (right
#' censored event) or 1 (observed event).
#' @param mu1_val Randomized control arm meanlog parameter used in call to \code{rlnorm()}.
#' @param mean_ratio_val Desired mean ratio between randomized experimental and control arms.
#' @param common_sd_val Randomized sdlog parameter used in call to \code{rlnorm()}.
#' Used in both randomized arms.
#' @param censor_value Value at which time-to-event data are right censored.
#' @param a0_val A power prior parameter ranging from 0 to 1, where 0
#' implies no information from historical data should be used, 1 implies all of
#' the information from historical data should be used. A value between 0 and 1
#' implies that a proportion of the information from historical data will be used.
#' @param alpha A number ranging between 0 and 1 that defines the acceptable Type 1
#' error rate. Default is 0.05.
#'
#' @return \code{lognormaltrialsimulator()} returns a vector of simulation results. The
#' first element is an estimated mean ratio, the second element is the estimated
#' variance of the log mean ratio, and the third element is a 0/1 variable indicator
#' whether or not the trial rejected the null hypothesis (1) or failed to reject
#' the null hypothesis (0).
#'
#' @examples
#' #None
#' @keywords internal
#' @noRd
lognormaltrialsimulator <- function(sample_size_val, histdata, mu1_val, mean_ratio_val, common_sd_val, censor_value,
a0_val, alpha) {
# --------------------------------------------------------------- #
# This function simulates a two-arm Bayesian trial where
# historical data is utilized in the parameter estimation.
# --------------------------------------------------------------- #
# First, Generate lognormal trial data given the user defined trial characteristics.
sampleranddata <- genlognormaldata(sample_size = sample_size_val, mu1 = mu1_val, mean_ratio = mean_ratio_val, common_sd = common_sd_val,
censor_value = censor_value)
# Make sure the trial data has at least one not right censored observation.
if (sum(sampleranddata$event_time == censor_value) == dim(sampleranddata)[1]) {
stop("Simulated trial data must have at least one observation that is not right censored.")
}
# Generate initial values for your call to optim()
initializemodel <- survival::survreg(survival::Surv(event_time, status) ~ treatment, dist = "lognormal", data = sampleranddata)
initialbeta0 <- initializemodel$coefficients[1]
initialbeta1 <- initializemodel$coefficients[2]
initiallogs <- log(initializemodel$scale)
# Generate the Bayesian CLT based parameter estimates needed for inference on mean ratio.
fitmod <- stats::optim(c(initialbeta0, initialbeta1, initiallogs), lognormalloglike, randdata = sampleranddata, histdata = histdata,
a0 = a0_val, method = "Nelder-Mead", hessian = TRUE)
#Extract model parameters and statistics
modparm <- fitmod$par
covarmat <- solve(fitmod$hessian)
lognormallogmeanratio <- modparm[2]
lognormalmeanratio <- exp(lognormallogmeanratio)
lower_lognormalmeanratio <- exp(lognormallogmeanratio - stats::qnorm(1 - alpha/2) * sqrt(covarmat[2, 2]))
upper_lognormalmeanratio <- exp(lognormallogmeanratio + stats::qnorm(1 - alpha/2) * sqrt(covarmat[2, 2]))
#Make a decision about the simulated trial, reject or fail to reject null hypothesis.
reject <- ifelse(((lower_lognormalmeanratio > 1) | (upper_lognormalmeanratio < 1)), 1, 0)
output <- c(lognormalmeanratio, covarmat[2, 2], reject)
#Return the mean ratio, the estimated variance of the log mean ratio, and the trial decision.
names(output) <- c("mean_ratio", "log_mean_ratio_var", "reject")
return(output)
}
#' Simulate a single randomized trial using a Lognormal outcome but not including any information from
#' historical controls.
#'
#' \code{lognormaltrialsimulatornohist()} function only used internally by
#' \code{simple_lognormal_sim()} function to estimate Lognormal model parameters
#' when clinical trial involves experimental and control groups but no historical control
#' data.
#'
#' The simulation of a trial with a Lognormal outcome involving no historical control data returns
#' an estimate of the mean ratio as well as an estimate of the log mean ratio variance.
#' Finally the simulation returns an indication of whether or not the simulated trial led to
#' a rejection of the null hypothesis (1) or not (0).
#'
#' \code{lognormaltrialsimulatornohist()} should not be called directly by user.
#'
#' @param sample_size_val Number of subjects per arm.
#' @param mu1_val meanlog parameter used in call to \code{rlnorm()}.
#' Used only in randomized control arm.
#' @param mean_ratio_val Desired mean ratio between experimental and control arms.
#' @param common_sd_val sdlog parameter used in call to \code{rlnorm()}.
#' Used in both arms.
#' @param censor_value Value at which time-to-event data are right censored.
#' @param alpha A number ranging between 0 and 1 that defines the acceptable Type 1
#' error rate. Default is 0.05.
#'
#' @return \code{lognormaltrialsimulatornohist()} returns a vector of simulation results. The
#' first element is an estimated mean ratio, the second element is the estimated
#' variance of the log mean ratio, and the third element is a 0/1 variable indicator
#' whether or not the trial rejected the null hypothesis (1) or failed to reject
#' the null hypothesis (0).
#'
#' @examples
#' #None
#' @keywords internal
#' @noRd
lognormaltrialsimulatornohist <- function(sample_size_val, mu1_val, mean_ratio_val, common_sd_val, censor_value, alpha) {
# --------------------------------------------------------------- #
# This function simulates a two-arm Bayesian trial where
# historical data is not utilized in the parameter estimation.
# --------------------------------------------------------------- #
# First, Generate lognormal trial data given the user defined trial characteristics.
sampleranddata <- genlognormaldata(sample_size = sample_size_val, mu1 = mu1_val, mean_ratio = mean_ratio_val, common_sd = common_sd_val,
censor_value = censor_value)
# Make sure the trial data has at least one not right censored observation.
if (sum(sampleranddata$event_time == censor_value) == dim(sampleranddata)[1]) {
stop("Simulated trial data must have at least one observation that is not right censored.")
}
# Generate the Bayesian CLT based parameter estimates needed for inference on mean ratio.
initializemodel <- survival::survreg(survival::Surv(event_time, status) ~ treatment, dist = "lognormal", data = sampleranddata)
#Extract model parameters and statistics
modparm <- initializemodel$coefficients
covarmat <- stats::vcov(initializemodel)
lognormallogmeanratio <- modparm[2]
lognormalmeanratio <- exp(lognormallogmeanratio)
lower_lognormalmeanratio <- exp(lognormallogmeanratio - stats::qnorm(1 - alpha/2) * sqrt(covarmat[2, 2]))
upper_lognormalmeanratio <- exp(lognormallogmeanratio + stats::qnorm(1 - alpha/2) * sqrt(covarmat[2, 2]))
#Make a decision about the simulated trial, reject or fail to reject null hypothesis.
reject <- ifelse(((lower_lognormalmeanratio > 1) | (upper_lognormalmeanratio < 1)), 1, 0)
output <- c(lognormalmeanratio, covarmat[2, 2], reject)
#Return the hazard ratio, the estimated variance of the log hazard ratio, and the trial decision.
names(output) <- c("mean_ratio", "log_mean_ratio_var", "reject")
return(output)
}
#' Repeated Two Arm Bayesian Clinical Trial Simulation with Historical Data and
#' Lognormal Outcome.
#'
#' \code{lognormal_sim()} function only used internally by \code{historic_sim()}
#' function to run a set of trial simulations involving historical
#' control data and a Lognormal outcome. User defined simulation parameters are
#' used to generate a set of trial scenarios. Each scenario is simulated multiple
#' times and then means are taken to calculate estimates of power, mean ratios,
#' and other user requested study summary statistics like variance of mean
#' ratio, bias (on mean ratio scale), and mse (on mean ratio scale).
#' The number of repeated simulations is defined by the user.
#'
#' \code{lognormal_sim()} should not be called directly by user.
#'
#' @param trial_reps Number of trials to replicate within each combination of
#' a0_val, subj_per_arm, effect_vals, and rand_control_diff. As the number
#' of trials increases, the precision of the estimate will increase. Default is
#' 100.
#' @param subj_per_arm A vector of sample sizes, all of which must be positive
#' integers.
#' @param a0_vals A vector of power prior parameters ranging from 0 to 1, where 0
#' implies no information from historical data should be used, 1 implies all of
#' the information from historical data should be used. A value between 0 and 1
#' implies that a proportion of the information from historical data will be used.
#' @param effect_vals A vector of mean ratios (randomized experimental over control),
#' all of which must be positive.
#' @param rand_control_diff For Lognormal outcomes this is a vector of mean ratios
#' (randomized controls over historical controls) that represent differences
#' between randomized and historical controls.
#' @param hist_control_data A dataset of historical data. Default is \code{NULL}.
#' Historical datasets must have 4 columns: id, treatment, event_time, and
#' status. The value of treatment should be 0. The values of event_time must
#' be positive. The values of status must be 0 (right censored event) or
#' 1 (observed event).
#' @param censor_value A single value at which right censoring occurs when
#' simulating randomized subject outcomes. Default is \code{NULL}, where
#' \code{NULL} implies no right censoring.
#' @param alpha A number ranging between 0 and 1 that defines the acceptable Type 1
#' error rate. Default is 0.05.
#' @param get_var A TRUE/FALSE indicator of whether an array of variance
#' estimates will be returned. Default is \code{FALSE}.
#' @param get_bias A TRUE/FALSE indicator of whether an array of bias
#' estimates will be returned. Default is \code{FALSE}.
#' @param get_mse A TRUE/FALSE indicator of whether an array of MSE
#' estimates will be returned. Default is \code{FALSE}.
#' @param quietly A TRUE/FALSE indicator of whether notes are printed
#' to output about simulation progress as the simulation runs. If
#' running interactively in RStudio or running in the R console,
#' \code{quietly} can be set to FALSE. If running in a Notebook or
#' knitr document, \code{quietly} needs to be set to TRUE. Otherwise
#' each note will be printed on a separate line and it will take up
#' a lot of output space. Default is \code{TRUE}.
#'
#' @return \code{lognormal_sim()} returns an S3 object of class bayes_ctd_array.
#'
#' @examples
#' #None
#' @keywords internal
#' @noRd
lognormal_sim <- function(trial_reps=100, subj_per_arm, a0_vals, effect_vals,
rand_control_diff, hist_control_data, censor_value,
alpha=0.05, get_var=FALSE, get_bias=FALSE, get_mse=FALSE,
quietly=TRUE) {
# --------------------------------------------------------------- #
# For a set of user specified scenarios (defined by combinations
# of user specified parameters), simulate "trial_reps" trials
# and estimate power, mean ratio estimate, and if requested by user:
# variance of mean ratio, bias, and mse. Using a Lognormal oucome
# and incorporating data from historical controls.
# --------------------------------------------------------------- #
# Need to take the historical data and generate distributional parameter estimates
histdata <- hist_control_data
hist_model <- survival::survreg(survival::Surv(event_time, status) ~ 1, dist = "lognormal", data = histdata)
initialmu1 <- hist_model$coefficients[1]
initialsd1 <- hist_model$scale
# Initialize arrays to hold power, var, mse, and bias estimate results as requested.
len_val <- length(rand_control_diff) * length(effect_vals) * length(a0_vals) * length(subj_per_arm)
power_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
est_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
if (get_mse == TRUE) {
mse_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
}
if (get_bias == TRUE) {
bias_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
}
if (get_var == TRUE) {
var_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
}
# Cycle through the passed values in rand_control_diff, effect_vals, a0_val, and subj_per_arm to generate the
# requested trial characteristics.
for (diffs in 1:length(rand_control_diff)) {
# Need to adjust the randomized control mean given the historical control mean and the mean ratios given in
# rand_control_diff. Note, mean ratios in rand_control_diff are ratios of lognormal means
# and not mu parameters of lognormal distributions (means of variables on log scale), so
# you first need to calculate the lognormal mean for the historical control group.
# lognormal mean for historical control group is:
lnmean1 <- exp(initialmu1 + 0.5 * initialsd1^2)
# given mean_ratio, lognormal for randomized control group is:
lnmean2 <- rand_control_diff[diffs] * lnmean1
# Now that we have the randomized control lognormal mean, we need to transform this
# back to a lognormal mu parameter:
# lognormal mu parameter for randomized control group is:
adjmu1 <- log(lnmean2) - 0.5 * initialsd1^2
for (effvals in 1:length(effect_vals)) {
for (a0vals in 1:length(a0_vals)) {
for (sizes in 1:length(subj_per_arm)) {
if (!quietly){
cat("\r", c(subj_per_arm[sizes], a0_vals[a0vals], effect_vals[effvals], rand_control_diff[diffs]))
}
# For each combination of rand_control_diff, effect_vals, a0_vals, and subj_per_arm, simulate the trial
#trial_reps times and then calculate the mean reject rate to estimate power. For bias, work on the
#mean ratio scale and take the mean of all differences between estimated mean ratios and the
#true mean ratio. For mse, calculate the mean of squared differences between the
#estimated mean ratios and the true mean ratio value.
collect <- matrix(rep(0, 3 * trial_reps), ncol = 3)
for (k in 1:trial_reps) {
# sample_size_val will be equal to both arms
collect[k, ] <- lognormaltrialsimulator(sample_size_val = subj_per_arm[sizes], histdata, mu1_val = adjmu1,
mean_ratio_val = effect_vals[effvals], common_sd_val = initialsd1, censor_value = censor_value,
a0_val = a0_vals[a0vals], alpha = alpha)
}
#collect is a matrix of data, mean ratio in 1st column, log mean ratio variance
# in second column, and a vector of 0/1s in third column indicating whether or
# not trial represented by row led to a rejection of null hypothesis (1) or not (0).
# Note that collect gets rewritten for each scenario.
colnames(collect) <- c("mean_ratio", "log_mean_ratio_var", "reject")
#Start calculating means for each scenarios and placing the means in the proper
# array. Every simulation will contain an array of power results and mean
# ratio estimates.
power_results[sizes, a0vals, effvals, diffs] <- mean(collect[, 3])
est_results[sizes, a0vals, effvals, diffs] <- mean(collect[, 1])
if (get_bias == TRUE) {
bias_results[sizes, a0vals, effvals, diffs] <- mean(collect[, 1] - effect_vals[effvals])
}
if (get_var == TRUE) {
var_results[sizes, a0vals, effvals, diffs] <- mean((collect[, 1]*sqrt(collect[, 2]))^2)
}
if (get_mse == TRUE) {
mse_results[sizes, a0vals, effvals, diffs] <- mean((collect[, 1] - effect_vals[effvals])^2)
}
if (!quietly){
cat("\r", " ")
}
}
}
}
}
cat("\n")
#Lines 534 through 837 simply apply names to the dimensions of array created by the
# simulation depending on values get_bias, get_var, and get_mse.
if (get_bias == FALSE & get_var == FALSE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results)
names(output) <- c("power", "est")
}
if (get_bias == FALSE & get_var == FALSE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, mse_results)
names(output) <- c("power", "est", "mse")
}
if (get_bias == TRUE & get_var == FALSE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, bias_results)
names(output) <- c("power", "est", "bias")
}
if (get_bias == TRUE & get_var == FALSE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, bias_results, mse_results)
names(output) <- c("power", "est", "bias", "mse")
}
if (get_bias == FALSE & get_var == TRUE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results)
names(output) <- c("power", "est", "var")
}
if (get_bias == FALSE & get_var == TRUE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results, mse_results)
names(output) <- c("power", "est", "var", "mse")
}
if (get_bias == TRUE & get_var == TRUE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results, bias_results)
names(output) <- c("power", "est", "var", "bias")
}
if (get_bias == TRUE & get_var == TRUE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results, bias_results, mse_results)
names(output) <- c("power", "est", "var", "bias", "mse")
}
#Create an list of results and apply the bayes_ctd_array class to the list, then
# return the output object.
class_out <- list(data = output, subj_per_arm = subj_per_arm, a0_vals = a0_vals, effect_vals = effect_vals, rand_control_diff = rand_control_diff, objtype= 'historic')
class(class_out) <- append("bayes_ctd_array", class(class_out))
return(class_out)
}
#' Repeated Two Arm Bayesian Clinical Trial Simulation with no Historical Data and
#' Lognormal Outcome.
#'
#' \code{simple_lognormal_sim()} function only used internally by \code{simple_sim()}
#' function to run a set of trial simulations involving no
#' historical control data and a Lognormal outcome. User defined simulation
#' parameters are used to generate a set of trial scenarios. Each scenario is
#' simulated multiple times and then means are taken to calculate estimates
#' of power, mean ratios, and other user requested study summary statistics
#' like variance of mean ratio, bias (on mean ratio scale), and
#' mse (on mean ratio scale). The number of repeated simulations is
#' defined by the user.
#'
#' \code{simple_lognormal_sim()} should not be called directly by user.
#'
#' @param trial_reps Number of trials to replicate within each combination of
#' subj_per_arm and effect_vals. As the number of trials increases, the
#' precision of the estimate will increase. Default is 100.
#' @param subj_per_arm A vector of sample sizes, all of which must be positive
#' integers.
#' @param effect_vals A vector of mean ratios (randomized experimental over control),
#' all of which must be positive.
#' @param mu1_val meanlog parameter value for randomized control arm. Used in call
#' to \code{rlnorm()}.
#' @param common_sd_val sdlog parameter value used in both randomized arms. Used in call to
#' \code{rlnorm()}.
#' @param censor_value A single value at which right censoring occurs when
#' simulating randomized subject outcomes. Default is \code{NULL}, where
#' \code{NULL} implies no right censoring.
#' @param alpha A number ranging between 0 and 1 that defines the acceptable Type 1
#' error rate. Default is 0.05.
#' @param get_var A TRUE/FALSE indicator of whether an array of variance
#' estimates will be returned. Default is \code{FALSE}.
#' @param get_bias A TRUE/FALSE indicator of whether an array of bias
#' estimates will be returned. Default is \code{FALSE}.
#' @param get_mse A TRUE/FALSE indicator of whether an array of MSE
#' estimates will be returned. Default is \code{FALSE}.
#' @param quietly A TRUE/FALSE indicator of whether notes are printed
#' to output about simulation progress as the simulation runs. If
#' running interactively in RStudio or running in the R console,
#' \code{quietly} can be set to FALSE. If running in a Notebook or
#' knitr document, \code{quietly} needs to be set to TRUE. Otherwise
#' each note will be printed on a separate line and it will take up
#' a lot of output space. Default is \code{TRUE}.
#'
#' @return \code{simple_lognormal_sim()} returns an S3 object of class bayes_ctd_array.
#'
#' @examples
#' #None
#' @keywords internal
#' @noRd
simple_lognormal_sim <- function(trial_reps, subj_per_arm, effect_vals, mu1_val, common_sd_val, censor_value, alpha,
get_var, get_bias, get_mse, quietly=TRUE) {
# --------------------------------------------------------------- #
# For a set of user specified scenarios (defined by combinations
# of user specified parameters), simulate "trial_reps" trials
# and estimate power, mean ratio estimate, and if requested by user:
# variance of mean ratio, bias, and mse. Using a Lognormal oucome
# but historical control data is not used.
# --------------------------------------------------------------- #
#The rand_control_diff and a0_val dimensions will be set to 1, and the value for
# rand_control_diff will be 1 and a0_val will be set to 0. All summaries will
# be set up to ignore these dimensions for simple (no historical data) simulations.
rand_control_diff <- 1
a0_vals <- 0
# Initialize arrays to hold power, var, mse, and bias estimate results as requested.
len_val <- length(rand_control_diff) * length(effect_vals) * length(a0_vals) * length(subj_per_arm)
power_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
est_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
if (get_mse == TRUE) {
mse_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
}
if (get_bias == TRUE) {
bias_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
}
if (get_var == TRUE) {
var_results <- array(rep(0, len_val), c(length(subj_per_arm), length(a0_vals), length(effect_vals), length(rand_control_diff)))
}
# Cycle through the passed values in rand_control_diff, effect_vals, a0_val, and subj_per_arm to generate the
# requested trial characteristics. Note that rand_control_diff is set to 1 and a0_val is set to 0.
for (diffs in 1:length(rand_control_diff)) {
for (effvals in 1:length(effect_vals)) {
for (a0vals in 1:length(a0_vals)) {
for (sizes in 1:length(subj_per_arm)) {
if (!quietly){
cat("\r", c(subj_per_arm[sizes], a0_vals[a0vals], effect_vals[effvals], rand_control_diff[diffs]))
}
# For each combination of rand_control_diff, effect_vals, a0_val, and subj_per_arm, simulate the trial
# trial_reps times and then calculate the mean reject rate to estimate power. For bias, work on the
#mean ratio scale and take the mean of all differences between estimated mean ratios and the
#true mean ratio. For mse, calculate the mean of squared differences between the estimated
#mean ratios and the true mean ratio value. Note that rand_control_diff is set to 1 and
#a0_val is set to 0.
collect <- matrix(rep(0, 3 * trial_reps), ncol = 3)
for (k in 1:trial_reps) {
# sample_size_val will be equal to both arms
collect[k, ] <- lognormaltrialsimulatornohist(sample_size_val = subj_per_arm[sizes], mu1_val = mu1_val,
mean_ratio_val = effect_vals[effvals], common_sd_val = common_sd_val, censor_value = censor_value,
alpha = alpha)
}
#collect is a matrix of data, mean ratio in 1st column, log mean ratio variance
# in second column, and a vector of 0/1s in third column indicating whether or
# not trial represented by row led to a rejection of null hypothesis (1) or not (0).
# Note that collect gets rewritten for each scenario.
colnames(collect) <- c("mean_ratio", "log_mean_ratio_var", "reject")
#Start calculating means for each scenarios and placing the means in the proper
# array. Every simulation will contain an array of power results and mean
# ratio estimates.
power_results[sizes, a0vals, effvals, diffs] <- mean(collect[, 3])
est_results[sizes, a0vals, effvals, diffs] <- mean(collect[, 1])
if (get_bias == TRUE) {
bias_results[sizes, a0vals, effvals, diffs] <- mean(collect[, 1] - effect_vals[effvals])
}
if (get_var == TRUE) {
var_results[sizes, a0vals, effvals, diffs] <- mean((collect[, 1]*sqrt(collect[, 2]))^2)
}
if (get_mse == TRUE) {
mse_results[sizes, a0vals, effvals, diffs] <- mean((collect[, 1] - effect_vals[effvals])^2)
}
if (!quietly){
cat("\r", " ")
}
}
}
}
}
cat("\n")
#Lines 970 through 1273 simply apply names to the dimensions of array created by the
# simulation depending on values get_bias, get_var, and get_mse.
if (get_bias == FALSE & get_var == FALSE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results)
names(output) <- c("power", "est")
}
if (get_bias == FALSE & get_var == FALSE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, mse_results)
names(output) <- c("power", "est", "mse")
}
if (get_bias == TRUE & get_var == FALSE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, bias_results)
names(output) <- c("power", "est", "bias")
}
if (get_bias == TRUE & get_var == FALSE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, bias_results, mse_results)
names(output) <- c("power", "est", "bias", "mse")
}
if (get_bias == FALSE & get_var == TRUE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results)
names(output) <- c("power", "est", "var")
}
if (get_bias == FALSE & get_var == TRUE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results, mse_results)
names(output) <- c("power", "est", "var", "mse")
}
if (get_bias == TRUE & get_var == TRUE & get_mse == FALSE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results, bias_results)
names(output) <- c("power", "est", "var", "bias")
}
if (get_bias == TRUE & get_var == TRUE & get_mse == TRUE) {
if (length(subj_per_arm) == 1) {
dimnames(power_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(power_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(power_results)[[2]] <- as.character(a0_vals)
dimnames(power_results)[[3]] <- as.character(effect_vals)
dimnames(power_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(est_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(est_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(est_results)[[2]] <- as.character(a0_vals)
dimnames(est_results)[[3]] <- as.character(effect_vals)
dimnames(est_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(bias_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(bias_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(bias_results)[[2]] <- as.character(a0_vals)
dimnames(bias_results)[[3]] <- as.character(effect_vals)
dimnames(bias_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(var_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(var_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(var_results)[[2]] <- as.character(a0_vals)
dimnames(var_results)[[3]] <- as.character(effect_vals)
dimnames(var_results)[[4]] <- as.character(rand_control_diff)
if (length(subj_per_arm) == 1) {
dimnames(mse_results)[[1]] <- list(as.character(subj_per_arm))
}
if (length(subj_per_arm) > 1) {
dimnames(mse_results)[[1]] <- as.character(subj_per_arm)
}
dimnames(mse_results)[[2]] <- as.character(a0_vals)
dimnames(mse_results)[[3]] <- as.character(effect_vals)
dimnames(mse_results)[[4]] <- as.character(rand_control_diff)
output <- list(power_results, est_results, var_results, bias_results, mse_results)
names(output) <- c("power", "est", "var", "bias", "mse")
}
#Create an list of results and apply the bayes_ctd_array class to the list, then
# return the output object.
class_out <- list(data = output, subj_per_arm = subj_per_arm, a0_vals = 0, effect_vals = effect_vals, rand_control_diff = 1, objtype= 'simple')
class(class_out) <- append("bayes_ctd_array", class(class_out))
return(class_out)
}
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