Pred_Prob_R: Bayesian predictive probability, given an observed difference...

In IDDI-BE/PhII_Bayes: Bayesian tools for PhII clinical trial design

Description

This function calculates the Bayesian predictive probability P(Delta>Dcut|N_exp,N_ctrl,n1_exp,n1_ctrl,x1) with Delta=Delta_exp-Delta_ctrl

Usage

Pred_Prob_R(
  p_exp,
  p_ctrl,
  N_exp,
  N_ctrl,
  n1_exp,
  n1_ctrl,
  distrisize = 1000,
  nsim = 1000,
  Dcut,
  PostProb,
  beta_par_exp,
  beta_par_ctrl,
  printprogress = T
)

Arguments

p_exp	observed proportion experimental arm
p_ctrl	observed proportion control arm
N_exp	number of patients in experimental arm (scalar) at end of study
N_ctrl	number of patients in control arm (scalar) at end of study
n1_exp	number of patients in experimental arm (scalar) at interim
n1_ctrl	number of patients in control arm (scalar) at interim
distrisize	Size of sampled distributions (the larger, the better)
nsim	Number of simulation to sample from predictive distribution
Dcut	True difference between two proportions (can be a vector)
PostProb	Threshold for outcome at the end of the study, in terms of Bayesian posterior probability P(theta>Dcut|x1+x2)>PostProb, with x1 the difference in proportions at interim and x2 at the final
beta_par_exp	two shape parameters c(alpha,beta) for prior beta distribution experimental arm
beta_par_ctrl	two shape parameters c(alpha,beta) for prior beta distribution control arm
printprogress	print progress bar (logical)

Value

Predictive Probability for outcome at the end of the study

Examples

Pred_Prob_R(p_exp=0.475,p_ctrl=0.475-0.08,N_exp=100,N_ctrl=100,n1_exp=50,n1_ctrl=50,
distrisize=10^3,nsim=10^3,PostProb=0.83,Dcut=0,beta_par_exp=c(1,1),beta_par_ctrl=c(1,1))

IDDI-BE/PhII_Bayes documentation built on May 19, 2021, 3:04 p.m.