optimal_tte_generic: Generic function for optimal planning of time-to-event endpoints

Description

Generic function for optimal planning of time-to-event endpoints

Usage

optimal_tte_generic(
  w,
  hr1,
  hr2,
  id1,
  id2,
  d2min,
  d2max,
  stepd2,
  hrgomin,
  hrgomax,
  stephrgo,
  alpha,
  beta,
  xi2,
  xi3,
  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, see this Shiny application for the choice of weights
hr1: first assumed true treatment effect on HR scale for prior distribution
hr2: second assumed true treatment effect on HR scale for prior distribution
id1: amount of information for hr1 in terms of number of events
id2: amount of information for hr2 in terms of number of events
d2min: minimal number of events for phase II
d2max: maximal number of events for phase II
stepd2: step size for the optimization over d2
hrgomin: minimal threshold value for the go/no-go decision rule
hrgomax: maximal threshold value for the go/no-go decision rule
stephrgo: step size for the optimization over HRgo
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 by Schoenfeld's formula (Schoenfeld 1981)
xi2: assumed event rate for phase II, used for calculating the sample size of phase II via n2 = d2/xi2
xi3: event rate for phase III, used for calculating the sample size of phase III in analogy to xi2
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 HR scale, default: 1
stepm1: lower boundary for effect size category "medium" in HR scale = upper boundary for effect size category "small" in HR scale, default: 0.95
stepl1: lower boundary for effect size category "large" in HR scale = upper boundary for effect size category "medium" in HR 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
fixed: choose if true treatment effects are fixed or random, if TRUE hr1 is used as a fixed effect and hr2 is ignored
num_cl: number of clusters used for parallel computing, default: 1