sim_magnusson_turnbull: Simulations of trials with GSED

Description

sim_magnusson_turnbull is used to simulate clincal trials with GSED for different type of outcome (survival, binary, continuous).

Usage

sim_magnusson_turnbull(K_stages, N_subsets, f, l, u, ratio_Delta_star_d1, type_outcome, 
param_outcome=NA, n_max=NA, incl_rate=NA, mean_cur_c=NA, HR=NA, nb_required=NA, 
nmax_wait=+Inf, ordering, increasing_theta=FALSE, nsim=1000, seed=42,
nsim_tot=NA, num_sc=1, updateProgress=NULL)

Value

A list is returned composed of:

prob_rejec: Percentage of simulated trials (estimated probability) to reject any subgroup.
prob_accep: Percentage of simulated trials (estimated probability) to accept the null hypothesis, that is there is no treatment effect in any subgroup.
list_keep: A list of the different subgroups that were selected across all simulated trials.
pct_keep: Percentage of selection of each subgroup of list_keep across all simulated trials.
rejec_stage: Vector of percentage of simulated trials (estimated probability) to reject any subgroup at each stage.
accep_stage: Vector of percentage of simulated trials (estimated probability) to accept the null hypothesis (that is there is no treatment effect in any subgroup) at each stage.
mean_pat: Mean number of patients included across all simulated trials.
mean_duration: If type_outcome is equal to "survival", the trial mean duration across all simulated trials is also returned.

Arguments

K_stages: Integer indicating the number of stages in the design.
N_subsets: Integer representing the number of possible subgroups.
f: Vector containing the prevalence rates of each subgroup. Must be of length N_subsets.
l: Vector containing the lower boundaries for stagewise decisions. Must be of length K_stages.
u: Vector containing the upper boundaries for stagewise decisions. Must be of length K_stages.
ratio_Delta_star_d1: Vector containing the ratio between the (observed Fisher) information increments at each stage >1 with the (observed Fisher) information at stage 1. Must be of length K_stages-1.
type_outcome: A string containing the type of outcome, either "survival", "binary", or "continuous".
param_outcome: Must be supplied only if type_outcome is equal to "binary" or "continuous". The parameters supplied for the binary outcome must be a list of one element containing a matrix of size 2xN_subsets. The parameters supplied for the continuous outcome must be a list of two elements containing two matrices of size 2xN_subsets. The matrices should contain probabilities of response, or the means and variances respectively, for in row control or treatment, and in column the subgroup number.
n_max: Integer representing the maximum number of patients to enroll in a trial. Must be supplied only if type_outcome is equal to "binary" or "continuous", will be ignored otherwise.
incl_rate: Number representing the inclusion rate. Must be supplied only if type_outcome is equal to "survival", will be ignored otherwise.
mean_cur_c: Number representing the median survival for the control group. Must be supplied only if type_outcome is equal to "survival", will be ignored otherwise.
HR: Vector containing the expected hazard ratios for each subgroup. Must be of length N_subsets. Must be supplied only if type_outcome is equal to "survival", will be ignored otherwise.
nb_required: Integer indicating the maximum number of events required. Must be supplied only if type_outcome is equal to "survival", will be ignored otherwise.
nmax_wait: For type_outcome equal to "survival" only, will be ignored otherwise. If specified, maximum number of patients to include in the trial, the inclusions will be stopped when this number is achieved and trial will pursue until the number of events required is achieved. Must be superior to nb_required. Default value is +Inf.
ordering: Boolean indicating if the subgroups (theta) are ordered.
increasing_theta: Boolean indicating if greater values of theta parameters represent better treatment effects. The default value is set at FALSE.
nsim: Integer indicating the number of trials to simulate. The default value is set at 1000.
seed: Interger representing the seed. The default value is set at 42.
nsim_tot: (for Rshiny application)
num_sc: (for Rshiny application)
updateProgress: (for Rshiny application)

Author

Marie-Karelle Riviere-Jourdan eldamjh@gmail.com

References

Baldur P. Magnusson and Bruce W. Turnbull. Group sequential enrichment design incorporating subgroup selection. Statistics in Medicine, 2013. <doi:10.1002/sim.5738>

Examples

Run this code

#For testing purpose only, larger number of simulations required (see in comments below)
sim_magnusson_turnbull(K_stages=2, N_subsets=3, f=c(0.6,0.2,0.2), l=c(0.7962, 2.5204), 
u=c(2.7625, 2.5204), ratio_Delta_star_d1=c(1), type_outcome="binary", param_outcome=
list(matrix(c(0.4,0.4,0.4,0.6,0.6,0.6),nrow=2,ncol=3,byrow=TRUE)), n_max=1496, 
ordering=FALSE, nsim=2, seed=42)

#sim_magnusson_turnbull(K_stages=2, N_subsets=3, f=c(0.6,0.2,0.2), l=c(0.7962, 2.5204), 
#u=c(2.7625, 2.5204), ratio_Delta_star_d1=c(1), type_outcome="binary", param_outcome=
#list(matrix(c(0.4,0.4,0.4,0.6,0.6,0.6),nrow=2,ncol=3,byrow=TRUE)), n_max=1496, 
#ordering=FALSE, nsim=1000, seed=42)

#sim_magnusson_turnbull(K_stages=2, N_subsets=3, f=c(0.6,0.2,0.2), l=c(0.7962, 2.5204), 
#u=c(2.7625, 2.5204), ratio_Delta_star_d1=c(1), type_outcome="binary", param_outcome=
#list(matrix(c(0.5,0.5,0.5,0.5,0.5,0.5),nrow=2,ncol=3,byrow=TRUE)), n_max=1496, 
#ordering=FALSE, nsim=1000, seed=42)

#sim_magnusson_turnbull(K_stages=2, N_subsets=4, f=c(0.25,0.25,0.25,0.25), l=c(0.98,2.35), 
#u=c(2.59,2.35), ratio_Delta_star_d1=c(1), type_outcome="survival", incl_rate=1/28, 
#mean_cur_c=7/log(2), HR=c(0.8,0.8,0.8,0.8), nb_required=1030, ordering=TRUE, 
#increasing_theta=FALSE, nsim=1000, seed=42)

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