Calculate Bayesian Predictive Probability given interim data and posterior samples

Usage

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).

Value

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

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)