catalytic (version 0.1.0)

cat_cox: Catalytic Cox Proportional Hazards Model (COX) Fitting Function with Fixed Tau

Description

Fits a Catalytic Cox proportional hazards model for survival data with specified variance parameters and iterative coefficient estimation, with either CRE (Catalytic-regularized Estimator) or WME (Weighted Mixture Estimator) methods.

Usage

cat_cox(
  formula,
  cat_init,
  tau = NULL,
  method = c("CRE", "WME"),
  init_coefficients = NULL,
  tol = 1e-05,
  max_iter = 25
)

Value

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

coefficients

Estimated coefficient vector.

model

Fitted Cox model object (if using WME).

iteration_log

Matrix logging variance and coefficient values for each iteration(if using CRE).

iter

Number of iterations (if using CRE).

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.

tau

Optional numeric scalar controlling the weight of the synthetic data in the coefficient estimation, defaults to the number of predictors.

method

The estimation method, either "CRE" (Catalytic-regularized Estimator) or "WME" (Weighted Mixture Estimator).

init_coefficients

Initial coefficient values before iteration. Defaults to zero if not provided (if using CRE).

tol

Convergence tolerance for iterative methods. Default is 1e-5 (if using CRE).

max_iter

Maximum number of iterations allowed for convergence. Default is 25 (if using CRE).

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_cre <- cat_cox(
  formula = ~.,
  cat_init = cat_init, # Only accept object generated from `cat_cox_initialization`
  tau = 1, # Weight for synthetic data
  method = "CRE", # Choose from `"CRE"` or `"WME"`
  init_coefficients = rep(0, ncol(cat_init$x)), # Initial coefficient values (Only for `CRE`)
  tol = 1e-1, # Tolerance for convergence criterion  (Only for `CRE`)
  max_iter = 3 # Maximum number of iterations for convergence  (Only for `CRE`)
)
cat_model_cre

cat_model_wme <- cat_cox(
  formula = ~.,
  cat_init = cat_init, # Only accept object generated from `cat_cox_initialization`
  tau = 1, # Weight for synthetic data
  method = "WME"
)
cat_model_wme