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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

@param 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.

Details

The function performs Bayesian updating under a delayed-effect model: $$ h_T(t) = \begin{cases} \lambda_c, & t \le T \\ \lambda_c \times HR, & t > T \end{cases} $$

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

Examples

if (FALSE) { # \dontrun{
posterior_df <- update_priors(
  data = interim_data,
  control_distribution = "Exponential",
  control_model = control_prior_list,
  delay_SHELF = delay_shelf_obj,
  HR_SHELF = hr_shelf_obj,
  delay_param_dist = "lognormal",
  HR_param_dist = "lognormal",
  n_samples = 2000
)
} # }