optimal_binary_generic: Generic function for optimizing programs with binary endpoints

Description

Generic function for optimizing programs with binary endpoints

Usage

optimal_binary_generic(
  w,
  p0,
  p11,
  p12,
  in1,
  in2,
  n2min,
  n2max,
  stepn2,
  rrgomin,
  rrgomax,
  steprrgo,
  alpha,
  beta,
  c2,
  c3,
  c02,
  c03,
  K = Inf,
  N = Inf,
  S = -Inf,
  steps1 = 1,
  stepm1 = 0.95,
  stepl1 = 0.85,
  b1,
  b2,
  b3,
  gamma = 0,
  fixed = FALSE,
  num_cl = 1
)

Arguments

w: weight for mixture prior distribution
p0: assumed true rate of control group, see here for details
p11: assumed true rate of treatment group, see here for details
p12: assumed true rate of treatment group, see here for details
in1: amount of information for p11 in terms of sample size, see here for details
in2: amount of information for p12 in terms of sample size, see here for details
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
rrgomin: minimal threshold value for the go/no-go decision rule
rrgomax: maximal threshold value for the go/no-go decision rule
steprrgo: step size for the optimization over RRgo
alpha: one-sided significance level
beta: type II error rate; i.e. 1 - beta is the power for calculation of the number of events 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" in RR scale, default: 1
stepm1: lower boundary for effect size category "medium" in RR scale = upper boundary for effect size category "small" in RR scale, default: 0.95
stepl1: lower boundary for effect size category "large" in RR scale = upper boundary for effect size category "medium" in RR scale, default: 0.85
b1: expected gain for effect size category "small"
b2: expected gain for effect size category "medium"
b3: expected gain for effect size category "large"
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 random, if TRUE p11 is used as fixed effect for p1
num_cl: number of clusters used for parallel computing, default: 1