sim_magnusson_turnbull {GSED}

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)

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)

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.

Author(s)

Marie-Karelle Riviere-Jourdan [e-mail]

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

#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)