EPsProg_multiple_normal: Expected probability of a successful program for multiple endpoints and normally distributed outcomes

Description

This function calculates the probability that our drug development program is successful. Successful is defined as both endpoints showing a statistically significant positive treatment effect in phase III.

Usage

EPsProg_multiple_normal(
  kappa,
  n2,
  alpha,
  beta,
  Delta1,
  Delta2,
  sigma1,
  sigma2,
  step11,
  step12,
  step21,
  step22,
  in1,
  in2,
  fixed,
  rho,
  rsamp
)

Value

The output of the function EPsProg_multiple_normal() is the expected probability of a successfull program, when going to phase III.

Arguments

kappa: threshold value for the go/no-go decision rule;
n2: total sample size for phase II; must be even number
alpha: significance level
beta: 1-beta power for calculation of sample size for phase III
Delta1: assumed true treatment effect given as difference in means for endpoint 1
Delta2: assumed true treatment effect given as difference in means for endpoint 2
sigma1: standard deviation of first endpoint
sigma2: standard deviation of second endpoint
step11: lower boundary for effect size for first endpoint
step12: lower boundary for effect size for second endpoint
step21: upper boundary for effect size for first endpoint
step22: upper boundary for effect size for second endpoint
in1: amount of information for Delta1 in terms of sample size
in2: amount of information for Delta2 in terms of sample size
fixed: choose if true treatment effects are fixed or random, if TRUE then Delta1 is used as fixed effect
rho: correlation between the two endpoints
rsamp: sample data set for Monte Carlo integration