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

calibrate_BPP_timing.Rd

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 = 50
)

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

Examples



#' set.seed(123)
control_model = list(dist = "Exponential",
                     parameter_mode = "Distribution",
                     t1 = 12,
                     t1_Beta_a = 20,
                     t1_Beta_b = 32)

effect_model = list(delay_SHELF = SHELF::fitdist(c(5.5, 6, 6.5),
                    probs = c(0.25, 0.5, 0.75), lower = 0, upper = 12),
                    delay_dist = "gamma",
                    HR_SHELF = SHELF::fitdist(c(0.5, 0.6, 0.7),
                    probs = c(0.25, 0.5, 0.75), lower = 0, upper = 1),
                    HR_dist = "gamma",
                    P_S = 1,
                    P_DTE = 0)

recruitment_model <- list(method = "power", period = 12, power = 1)

IA_model = list(events = 20, IF = 0.5)

analysis_model = list(method = "LRT",
                      alpha = 0.025,
                      alternative_hypothesis = "one.sided")

timing <- calibrate_BPP_timing(n_c = 15, n_t = 15,
                     control_model = control_model,
                     effect_model = effect_model,
                     recruitment_model = recruitment_model,
                     IA_model = IA_model,
                     analysis_model = analysis_model,
                     n_sims = 2)