optimal_normal_generic: Generic function for optimizing normally distributed endpoints

Description

Generic function for optimizing normally distributed endpoints

Usage

optimal_normal_generic(
  w,
  Delta1,
  Delta2,
  in1,
  in2,
  a,
  b,
  n2min,
  n2max,
  stepn2,
  kappamin,
  kappamax,
  stepkappa,
  alpha,
  beta,
  c2,
  c3,
  c02,
  c03,
  K = Inf,
  N = Inf,
  S = -Inf,
  steps1 = 0,
  stepm1 = 0.5,
  stepl1 = 0.8,
  b1,
  b2,
  b3,
  gamma = 0,
  fixed = FALSE,
  num_cl = 1
)

Arguments

w: weight for mixture prior distribution
Delta1: assumed true prior treatment effect measured as the standardized difference in means, see here for details
Delta2: assumed true prior treatment effect measured as the standardized difference in means, see here for details
in1: amount of information for Delta1 in terms of sample size, see here for details
in2: amount of information for Delta2 in terms of sample size, see here for details
a: lower boundary for the truncation of the prior distribution
b: upper boundary for the truncation of the prior distribution
n2min: minimal total sample size for phase II; must be an even number
n2max: maximal total sample size for phase II, must be an even number
stepn2: step size for the optimization over n2; must be an even number
kappamin: minimal threshold value kappa for the go/no-go decision rule
kappamax: maximal threshold value kappa for the go/no-go decision rule
stepkappa: step size for the optimization over the threshold value kappa
alpha: one-sided significance level
beta: type II error rate; i.e. 1 - beta is the power for calculation of the sample size for phase III
c2: variable per-patient cost for phase II in 10^5 $
c3: variable per-patient cost for phase III in 10^5 $
c02: fixed cost for phase II in 10^5 $
c03: fixed cost for phase III in 10^5 $
K: constraint on the costs of the program, default: Inf, e.g. no constraint
N: constraint on the total expected sample size of the program, default: Inf, e.g. no constraint
S: constraint on the expected probability of a successful program, default: -Inf, e.g. no constraint
steps1: lower boundary for effect size category "small", default: 0
stepm1: lower boundary for effect size category "medium" = upper boundary for effect size category "small" default: 0.5
stepl1: lower boundary for effect size category "large" = upper boundary for effect size category "medium", default: 0.8
b1: expected gain for effect size category "small" in 10^5 $
b2: expected gain for effect size category "medium" in 10^5 $
b3: expected gain for effect size category "large" in 10^5 $
gamma: to model different populations in phase II and III choose gamma != 0, default: 0, see here for details
fixed: choose if true treatment effects are fixed or following a prior distribution, if TRUE Delta1 is used as fixed effect
num_cl: number of clusters used for parallel computing, default: 1