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Function to calculate the 'optimal' BPP threshold value

calibrate_BPP_thresholds.Rd

Function to calculate the 'optimal' BPP threshold value

Usage

calibrate_BPP_thresholds(
  n_c,
  n_t,
  control_model,
  effect_model,
  recruitment_model,
  IA_model,
  analysis_model,
  data_generating_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).

data_generating_model

A named list specifying the data-generating mechanisms

  • null_model: lambda_c

  • fixed_delay: lambda_c, delay_time and post_delay_HR

  • no_delay: lambda_c and HR

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