Design or evaluate a one-stage single-arm Bayes factor trial — design_singlearm_onestage

Calibrates or evaluates a one-stage single-arm Bayes factor design for a binary endpoint.

Usage

design_singlearm_onestage_bf(
  n_min,
  n_max,
  k,
  k_ce = NULL,
  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,
  algorithm = c("optimal", "manual"),
  n = NULL,
  power_cushion = 0,
  sustain_n = 10L,
  ...
)

Arguments

n_min: Integer. Minimum admissible sample size.
n_max: Integer. Maximum admissible sample size.
k: Numeric scalar greater than 0. Evidence threshold on the \(BF_{01}\) scale for efficacy, used for power and type-I error.
k_ce: Optional numeric scalar greater than 1. Threshold on the \(BF_{01}\) scale used for CE(H0) / PCE(H0). Must be supplied when target_ce_h0 > 0.
p0: Numeric scalar in \((0,1)\). Null response probability.
a0, b0: Positive numeric scalars. Beta analysis-prior parameters under \(H_0\).
a1, b1: Positive numeric scalars. Beta analysis-prior parameters under \(H_1\).
dp: Optional numeric scalar in \((0,1)\). Fixed point alternative used for frequentist power calculations under \(H_1\).
da0, db0: Positive numeric scalars. Beta design-prior parameters under \(H_0\).
da1, db1: Positive numeric scalars. Beta design-prior parameters under \(H_1\).
type: Character string specifying the Bayes-factor test. One of "point" or "direction".
calibration: Character string specifying the calibration mode. One of "Bayesian", "frequentist", "hybrid", or "full".
target_power: Numeric scalar in \((0,1)\). Target corrected Bayesian power.
target_type1: Numeric scalar in \((0,1)\). Target corrected Bayesian type-I error.
target_ce_h0: Numeric scalar in \([0,1)\). Optional lower bound on the corrected Bayesian probability of compelling evidence in favour of \(H_0\).
target_freq_power: Numeric scalar in \((0,1)\). Target corrected frequentist power at dp.
target_freq_type1: Numeric scalar in \((0,1)\). Target corrected frequentist type-I error at \(p = p_0\).
algorithm: Character string specifying whether the design should be optimized or only evaluated.
n: Optional integer sample size used when algorithm = "manual".
power_cushion: Optional additive cushion applied to the power targets in the optimizer.
sustain_n: Non-negative integer. A candidate design is considered feasible only if the relevant operating characteristics satisfy their target constraints at the candidate sample size and for the next sustain_n larger sample sizes, subject to the search range. This also applies to the CE(H0) constraint when target_ce_h0 > 0.
...: Reserved for future extensions.

Value

An object of class "singlearm_onestage_bf_design".

Details

The design uses the Bayes factor \(BF_{01}\). Small values of \(BF_{01}\) indicate evidence against \(H_0\), so efficacy is concluded when \(BF_{01} \le k\). Large values indicate evidence in favour of \(H_0\), and the optional CE(H0) / PCE(H0) constraint is evaluated using the separate threshold k_ce.

Analysis priors are specified separately under \(H_0\) and \(H_1\) via a0, b0, a1, b1. Design priors are specified separately under \(H_0\) and \(H_1\) via da0, db0, da1, db1.