posp_normal: Probability of a successful program, when going to phase III for multiple endpoint with normally distributed outcomes

Description

After getting the "go"-decision to go to phase III, i.e. our results of phase II are over the predefined threshold kappa, this function calculates the probability, that our program is successfull, i.e. that both endpoints show a statistically significant positive treatment effect in phase III.

Usage

posp_normal(
  kappa,
  n2,
  alpha,
  beta,
  Delta1,
  Delta2,
  sigma1,
  sigma2,
  in1,
  in2,
  fixed,
  rho,
  rsamp
)

Value

The output of the function posp_normal() is the probability of a successful 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
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 Delta1 is used as fixed effect
rho: correlation between the two endpoints
rsamp: sample data set for Monte Carlo integration