simulation_main_GAUSSIAN {RARfreq}R Documentation

Doubly Adaptive Biased Coin Design with Simulated Data (Gaussian Responses)

Description

Allocates patients to one of treatments based on the doubly adaptive biased coin design with simulated data.

Usage

simulation_main_GAUSSIAN(n, nstart, mu, sd, nstop, replication, group_allo,
 rho_func_index, rho_func, alpha, sig_level)

Arguments

n

The number of patients. The default is 500.

nstart

Burn-in sample size of each arm. The default is n/20.

mu

A vector of mean response for each treatment arm (where the first element refers to the control arm). The length of mu should correspond to the number of arms. The default is mu = c(4.5,5).

sd

A vector of response standard deviations for each treatment arm. (where the first element refers to the control arm). The length of sd should correspond to the number of arms. The default is sd = c(1.32, 0.72).

nstop

A vector of stopping cap of sample size for each arm. The trial stops if at least one arm reaches the corresponding cap. The default is NULL, which means no cap.

replication

the number of replications of the simulation. The default is 100.

group_allo

A number or a vector of group size(s) for allocation. If a number is given, the allocation ratios will be updated for each batch of group_allo samples. If a vector is given, the allocation ratios will be updated sequentially in group according to the vector. Any value greater than n will be omitted. The default is group_allo=1, which is the same as group_allo = seq(nstart*length(p)+1,n).

rho_func_index

Supply a number of 1 or 2 indicting the allocation function to use. 1 = Zhang-Rosenberger allocation (2-arm allocation only); 2 (default) = Neyman allocation.

rho_func

Supply a user-specified allocation function of Mean_RK and SD_RK when rho_func_index is NULL. Default is NULL.

alpha

Supply a number indicating the subscripts of the probability function. The default is 2.

sig_level

Significant level. The default is 0.05.

Details

'simulation_main_GAUSSIAN' can sample response and adaptively randomize subjects group by group.

Value

Examples


## Default method
simulation_main_GAUSSIAN(
n = 500,
nstart = round(500 / 20),
mu = c(4.5,5),
sd = c(1.32,0.72),
nstop=c(500,500),
replication = 5,
group_allo = 1,
rho_func_index = 2,
rho_func = NULL,
alpha = 2,
sig_level = 0.05
)


[Package RARfreq version 0.1.5 Index]