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Update prior distributions using interim survival data

update_priors.Rd

This function updates elicited priors (defined through SHELF objects and parametric prior distributions) using interim survival data under a delayed-effect, piecewise-exponential model for the treatment arm and an exponential or Weibull model for the control arm.

Usage

update_priors(data, control_model, effect_model, n_samples = 1000)

Arguments

data

A data frame containing interim survival data with columns:

  • survival_time Observed time from randomisation to event/censoring.

  • status Event indicator (1 = event, 0 = censored).

  • group Group identifier (e.g., "Control", "Treatment").

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

n_samples

Number of posterior samples to generate (default: 1000).

Value

A data frame containing Monte Carlo samples from the updated (posterior) distribution of the model parameters. Columns normally include:

  • lambda_c Posterior samples for the control hazard parameter.

  • delay_time Posterior samples for the delay/changepoint time \(T\).

  • HR Posterior samples for the post-delay hazard ratio.

  • gamma_c (only if control_distribution = "Weibull") Posterior samples for the Weibull shape parameter.

Priors for lambda_c, T, and HR are constructed from elicited distributions using the SHELF framework, then updated through sampling-based posterior inference.

Examples

set.seed(123)
interim_data = data.frame(survival_time = runif(10, min = 0, max = 10),
status = rbinom(10, size = 1, prob = 0.5),
group = c(rep("Control", 5), rep("Treatment", 5)))
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)

posterior_df <- update_priors(
  data = interim_data,
  control_model = control_model,
  effect_model = effect_model,
  n_samples = 10)