cat_cox_bayes_joint: Bayesian Estimation for Catalytic Cox Proportional-Hazards...

In catalytic: Tools for Applying Catalytic Priors in Statistical Modeling

Bayesian Estimation for Catalytic Cox Proportional-Hazards Model (COX) with adaptive tau

Description

This function performs Bayesian estimation for a catalytic Cox proportional-hazards model (COX) using RStan by using adaptive tau. It allows users to estimate the coefficients and cumulative baseline hazard increments over specified time intervals with Bayesian sampling.

Usage

cat_cox_bayes_joint(
  formula,
  cat_init,
  hazard_beta = 2,
  tau_alpha = 2,
  tau_gamma = 1,
  chains = 4,
  iter = 2000,
  warmup = 1000,
  ...
)

Arguments

formula	A formula specifying the Cox model. Should at least include response variables (e.g. ~ .).
cat_init	A list generated from cat_cox_initialization.
hazard_beta	Numeric, default 2. Shape parameter for the Gamma distribution in the hazard model.
tau_alpha	Numeric, defaults 2. Scalar for the shape parameter of the Gamma-like function for tau.
tau_gamma	Numeric, defaults 1. Scalar for the scale parameter of the Gamma-like function for tau.
chains	Integer, default 4. Number of Markov chains to be run in the RStan sampling.
iter	Integer, default 2000. Number of iterations per chain in the RStan sampling.
warmup	Integer, default 1000. Number of warm-up (or burn-in) iterations for each chain.
...	Additional arguments passed to RStan’s rstan::sampling function.

Value

A list containing the values of all the arguments and the following components:

stan_data	A data list used for fitting RStan sampling model.
stan_model	Compiled RStan model object for Cox regression.
stan_sample_model	Fitted RStan sampling model containing posterior samples.
tau	Mean posterior estimates of tau value from stan_sample_model.
coefficients	Mean posterior estimates of model coefficients from stan_sample_model.
increment_cumulative_baseline_hazard	Mean posterior estimates of Estimated cumulative hazard increments over time intervals from stan_sample_model.

Examples

library(survival)
data("cancer")
cancer$status[cancer$status == 1] <- 0
cancer$status[cancer$status == 2] <- 1

cat_init <- cat_cox_initialization(
  formula = Surv(time, status) ~ 1, # formula for simple model
  data = cancer,
  syn_size = 100, # Synthetic data size
  hazard_constant = 0.1, # Hazard rate value
  entry_points = rep(0, nrow(cancer)), # Entry points of each observation
  x_degree = rep(1, ncol(cancer) - 2), # Degrees for polynomial expansion of predictors
  resample_only = FALSE, # Whether to perform resampling only
  na_replace = stats::na.omit # How to handle NA values in data
)

cat_model <- cat_cox_bayes_joint(
  formula = ~., # Should at least include response variables
  cat_init = cat_init, # Only accept object generated from `cat_cox_initialization`
  hazard_beta = 2, # Shape parameter for the Gamma distribution in the hazard model
  tau_alpha = 2, # Shape parameter of the Gamma-like function for tau
  tau_gamma = 1, # Scale parameter of the Gamma-like function for tau
  chains = 1, # Number of Markov chains to be run in the RStan sampling
  iter = 10, # Number of iterations per chain in the RStan sampling
  warmup = 5 # Number of warm-up (or burn-in) iterations for each chain
)
cat_model

catalytic documentation built on April 4, 2025, 5:51 a.m.