Optimal two-stage single-arm Bayes factor design — optimal_twostage_singlearm

Searches over admissible two-stage single-arm designs with a binary endpoint and returns the feasible design with smallest expected sample size under H0.

Usage

optimal_twostage_singlearm_bf(
  n1_min,
  n2_max,
  k,
  k_f,
  p0,
  a0 = 1,
  b0 = 1,
  a1 = 1,
  b1 = 1,
  dp = NA_real_,
  da0 = 1,
  db0 = 1,
  da1 = 1,
  db1 = 1,
  type = c("point", "direction"),
  calibration = c("Bayesian", "frequentist", "hybrid", "full"),
  target_power = 0.8,
  target_type1 = 0.05,
  target_ce_h0 = 0,
  target_freq_power = 0.8,
  target_freq_type1 = 0.05,
  power_cushion = 0
)

Arguments

n1_min: Minimum admissible interim sample size.
n2_max: Maximum admissible final sample size.
k: Efficacy threshold on the BF01 scale.
k_f: Futility threshold on the BF01 scale.
p0: Null response probability.
a0, b0: Beta analysis-prior parameters under H0.
a1, b1: Beta analysis-prior parameters under H1.
dp: Optional fixed point alternative used for frequentist power.
da0, db0: Beta design-prior parameters under H0.
da1, db1: Beta design-prior parameters under H1.
type: Character string; one of "point" or "direction".
calibration: Character string; one of "Bayesian", "frequentist", "hybrid", or "full".
target_power: Target corrected Bayesian power.
target_type1: Target corrected Bayesian type-I error.
target_ce_h0: Optional lower bound on corrected Bayesian compelling evidence in favour of H0.
target_freq_power: Target corrected frequentist power at dp.
target_freq_type1: Target corrected frequentist type-I error at p0.
power_cushion: Optional additive cushion for the fixed-sample power target in the first step of the search.

Value

A list describing the optimal design and search results.

Details

Analysis priors are specified separately under H0 and H1 via a0, b0, a1, b1. Design priors are specified separately under H0 and H1 via da0, db0, da1, db1.