This function performs simulation based trial design evaluations for a set of specified dose-response models
Usage
assessDesign(
n_patients,
mods,
prior_list,
sd,
n_sim = 1000,
alpha_crit_val = 0.05,
simple = TRUE,
reestimate = FALSE,
contr = NULL,
dr_means = NULL
)Arguments
- n_patients
Vector specifying the planned number of patients per dose group
- mods
An object of class "Mods" as specified in the DoseFinding package.
- prior_list
A prior_list object specifying the utilized prior for the different dose groups
- sd
A positive value, specification of assumed sd
- n_sim
Number of simulations to be performed
- 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 is 0.05.
- simple
Boolean variable defining whether simplified fit will be applied. Passed to the getModelFits function. Default FALSE.
- reestimate
Boolean variable defining whether critical value should be calculated with re-estimated contrasts (see getCritProb function for more details). Default FALSE
- contr
An object of class 'optContr' as created by the getContr() function. Allows specification of a fixed contrasts matrix. Default NULL
- dr_means
A vector, allows specification of individual (not model based) assumed effects per dose group. Default NULL
Value
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
if (interactive()) { # takes typically > 5 seconds
mods <- DoseFinding::Mods(linear = NULL,
linlog = NULL,
emax = c(0.5, 1.2),
exponential = 2,
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)
success_probabilities <- assessDesign(
n_patients = n_patients,
mods = mods,
prior_list = prior_list,
sd = sd,
n_sim = 1e2) # speed up exammple run time
success_probabilities
}