# Calculate Bayesian Predictive Probability given interim data and posterior samples ```r BPP_func( data, posterior_df, control_distribution = "Exponential", n_c_planned, n_t_planned, rec_time_planned, df_cens_time, censoring_model, analysis_model, n_sims = 500 ) ``` ## Arguments - `data`: A data frame containing interim survival data, censored at `df_cens_time`, with columns: * `time` Final observed/event time at the interim (on the analysis time scale). * `group` Treatment group indicator (e.g. "Control", "Treatment"). * `rec_time` Recruitment (calendar) time. * `pseudo_time` `time + rec_time` (calendar time at event/censoring). * `status` Event indicator at the interim (1 = event, 0 = censored). * `survival_time` Observed follow-up time from randomisation to event/censoring at the interim. - `posterior_df`: A data frame of posterior samples with columns: `lambda_c`, `delay_time` and `HR`, corresponding to the control hazard, the delay (changepoint) time and the post-delay hazard ratio, respectively. - `control_distribution`: Distributional form assumed for the control arm: either `"Exponential"` (default) or `"Weibull"`. - `n_c_planned`: Planned maximum number of patients in the control group. - `n_t_planned`: Planned maximum number of patients in the treatment group. - `rec_time_planned`: Planned maximum recruitment calendar time for the full trial. - `df_cens_time`: Calendar time at which `df` has been censored (interim analysis time). - `censoring_model`: A named list specifying the censoring mechanism for the future data: * `method`: one of `"Time"`, `"Events"`, or `"IF"`. * `time`, `events`, `IF`: parameters for the corresponding method. - `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 predictive simulations to run (default is 1000). ## Returns A single numeric value giving the Bayesian predictive probability of success at the final analysis under the specified design, censoring model and analysis model. ## Examples ```r set.seed(123) n <- 30 cens_time <- 15 time <- runif(n, 0, 12) rec_time <- runif(n, 0, 12) df <- data.frame( time = time, group = c(rep("Control", n/2), rep("Treatment", n/2)), rec_time = rec_time ) df$pseudo_time <- df$time + df$rec_time df$status <- df$pseudo_time < cens_time df$survival_time <- ifelse(df$status == TRUE, df$time, cens_time - df$rec_time) posterior_df <- data.frame(HR = rnorm(20, mean = 0.75, sd = 0.05), delay_time = rep(0, 20), lambda_c = rnorm(20, log(2)/9, sd = 0.01)) censoring_model = list(method = "Time", time = 25) analysis_model = list(method = "LRT", alpha = 0.025, alternative_hypothesis = "one.sided") BPP_outcome <- BPP_func(df, posterior_df, control_distribution = "Exponential", n_c_planned = n/2, n_t_planned = n/2, rec_time_planned = 12, df_cens_time = 15, censoring_model = censoring_model, analysis_model = analysis_model, n_sims = 10) ```

BPP_func function