Function to calculate the 'optimal' information fraction to calculate BPP

Function to calculate the 'optimal' information fraction to calculate BPP

Usage

calibrate_BPP_timing(
  n_c,
  n_t,
  control_model,
  effect_model,
  recruitment_model,
  IA_model,
  analysis_model,
  n_sims = 100
)

Arguments

n_c

Number of control patients

n_t

Number of treatment patients

control_model

A named list specifying the control arm survival distribution:

• dist: Distribution type ("Exponential" or "Weibull")

• parameter_mode: Either "Fixed" or "Distribution"

• fixed_type: If "Fixed", specify as "Parameters" or "Landmark"

• lambda, gamma: Scale and shape parameters

• t1, t2: Landmark times

• surv_t1, surv_t2: Survival probabilities at landmarks

• t1_Beta_a, t1_Beta_b, diff_Beta_a, diff_Beta_b: Beta prior parameters

effect_model

A named list specifying beliefs about the treatment effect:

• delay_SHELF, HR_SHELF: SHELF objects encoding beliefs

• delay_dist, HR_dist: Distribution types ("hist" by default)

• P_S: Probability that survival curves separate

• P_DTE: Probability of delayed separation, conditional on separation

recruitment_model

A named list specifying the recruitment process:

• method: "power" or "PWC"

• period, power: Parameters for power model

• rate, duration: Comma-separated strings for PWC model

IA_model

A named list specifying the censoring mechanism for the future data:

• events: Number of events which is 100% information fraction

• IF: The information fraction at which to censor and calculate BPP

analysis_model

A named list specifying the final analysis and decision rule:

• method: e.g. "LRT", "WLRT", or "MW".

• alpha: one-sided type I error level.

• alternative_hypothesis: direction of the alternative (e.g. "one.sided").

• rho, gamma, t_star, s_star: additional parameters for WLRT or MW (if applicable).

n_sims

Number of data sets to simulate (default is 100).

Value

A vector of length n_sims corresponding to the value of BPP for each simulated trial