max_FI {GSED} | R Documentation |
Maximum Fisher information
Description
max_FI
is used to estimate maximum Fisher information based on two power criteria.
- The first criterion consider the maxmimum Fisher information such that there is a pre-defined power to declare efficacy in the entire population for a given vector of parameters representing treatment effetcs in each subgroup.
- The second criterion consider the maxmimum Fisher information such that there is a pre-defined power to declare efficacy in at least one subgroup for a given vector of parameters representing treatment effetcs in each subgroup.
Usage
max_FI(K_stages, N_subsets, f, ratio_Delta_star_d1, l, u, type_outcome, param_theta,
pow, ordering, increasing_theta=FALSE, seed=42, n_trials, rule, 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 |
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 |
l |
Vector containing the lower boundaries for stagewise decisions. Must be of length |
u |
Vector containing the upper boundaries for stagewise decisions. Must be of length |
type_outcome |
A string containing the type of outcome, either "survival", "binary", or "continuous". |
param_theta |
Vector of parameters representing treatment effects in each subgroup. Must satisfy the properties detailed in Magnusson and Turnbull's article (reparametrization can be needed). |
pow |
Value representing the desired power. |
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. |
seed |
Interger representing the seed. The default value is set at 42. |
n_trials |
Integer indicating the number of trials to simulate. |
rule |
Integer with value either 1 or 2 for power criteria detailed in description section (1 for entire population, 2 for at least one subgroup). |
updateProgress |
(for Rshiny application) |
Value
A value representing the maximum Fisher information is returned.
Author(s)
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
theta_assumption = list(matrix(c(0.4,0.6,0.4,0.6,0.4,0.6),nrow=2,ncol=3))
#For testing purpose only, larger number of simulations required (see in comments below)
max_FI(K_stages=2, N_subsets=3, f=c(0.6,0.2,0.2), ratio_Delta_star_d1=c(1), l=c(0.7962, 2.5204),
u=c(2.7625, 2.5204), type_outcome="binary", param_theta=theta_assumption, pow=0.9,
ordering=FALSE, increasing_theta=FALSE, seed=140691, n_trials=3, rule=1)
#max_FI(K_stages=2, N_subsets=3, f=c(0.6,0.2,0.2), ratio_Delta_star_d1=c(1), l=c(0.7962, 2.5204),
#u=c(2.7625, 2.5204), type_outcome="binary", param_theta=theta_assumption, pow=0.9,
#ordering=FALSE, increasing_theta=FALSE, seed=140691, n_trials=10000000, rule=1)
#max_FI(K_stages=2, N_subsets=3, f=c(0.6,0.2,0.2), ratio_Delta_star_d1=c(1), l=c(0.7962, 2.5204),
#u=c(2.7625, 2.5204), type_outcome="binary", param_theta=theta_assumption, pow=0.9,
#ordering=FALSE, increasing_theta=FALSE, seed=140691, n_trials=10000000, rule=2)