brada {brada}

brada

Description

Performs a Bayesian response-adaptive design analysis for trials with a binary endpoint.

Usage

brada(a0=1,b0=1,Nmax=40,batchsize=5,nInit,p_true,p0,p1,
theta_T=0.90,theta_L=0.1,theta_U=1,nsim=100,
seed=42,method="PP",refFunc="flat",nu=0,
shape1=1,shape2=1,truncation=1,cores=2)

Arguments

a0

shape1 parameter of the beta prior.

b0

shape2 parameter of the beta prior.

Nmax

Maximum trial size.

batchsize

sample size after which an interim analysis is performed.

nInit

Initial sample size at which the first interim analysis is performed.

p_true

True binary response probability used for simulation.

p0

Right boundary of the null hypothesis to be tested.

p1

Left boundary of the alternative hypothesis to be tested.

theta_T

Threshold used in the designs for including trajectories as evidential.

theta_L

Stopping threshold for futility.

theta_U

Stopping threshold for efficacy.

nsim

Number of Monte Carlo iterations.

seed

Random number generator seed.

cores

Number of CPU cores to be used for computation. Defaults to 2, but 4 or larger is recommended.

method

Can be either "PP" or "PPe", depending on whether the predictive probability approach or the predictive evidence value design is desired. Note that the former is a special case of the latter.

refFunc

A string, either "flat", "beta", "binaryStep", "relu", "palu" or "lolu". See vignettes for explanation.

nu

A numeric value larger or equal to zero, indicating which evidence threshold if used in the predictive evidence value design.

shape1

shape1 parameter of the beta reference function, if used.

shape2

shape2 parameter of the beta reference function, if used.

truncation

Truncation point in case an artificial neural network reference function is used.

Value

Returns an object of class brada.

Author(s)

Riko Kelter

Examples

pp_design = brada(Nmax = 30, batchsize = 5, nInit = 10, 
               p_true = 0.2 , p0 = 0.2, p1 = 0.2, 
               nsim = 10,
               a0 = 1, b0 = 1, 
               theta_T = 0.90, theta_L = 0.1, theta_U = 1, 
               method = "PP",
               cores = 2)
summary(pp_design)