Conditional Logistic Regression with Individual-level External Data (CLR-Indi)

Fits a series of Conditional Logistic Regression models that integrate external individual-level data via a composite likelihood weight etas, suitable for matched case-control studies.

Usage

ncc_indi(
  y_int,
  z_int,
  stratum_int,
  y_ext,
  z_ext,
  stratum_ext,
  etas,
  max_iter = 100,
  tol = 1e-07,
  message = FALSE
)

Arguments

y_int

Numeric vector of binary outcomes for the internal dataset (0 = control, 1 = case).

z_int

Numeric matrix of covariates for the internal dataset.

stratum_int

Numeric or factor vector defining the matched sets (strata) for the internal dataset. Required.

y_ext

Numeric vector of binary outcomes for the external dataset (0 = control, 1 = case).

z_ext

Numeric matrix of covariates for the external dataset. Must have the same number of columns as z_int.

stratum_ext

Numeric or factor vector defining the matched sets (strata) for the external dataset. Required.

etas

Numeric vector of nonnegative external weights. eta = 0 gives an internal-only fit.

max_iter

Maximum number of Newton-Raphson iterations. Default 100.

tol

Convergence tolerance. Default 1e-7.

message

Logical. If TRUE, shows a progress bar. Default FALSE.

Value

An object of class "ncc_indi" and "cox_indi" containing the estimation results for each eta value. See cox_indi for a description of the return components.

Details

This function maps the Conditional Logistic Regression problem to a Cox PH model with fixed event time \(T=1\) and event indicator \(\delta=y\) for both the internal and external matched case-control datasets, then calls cox_indi as the core engine.

The fitted objective is $$\ell_\eta(\beta) = \ell_{\text{int}}(\beta) + \eta \, \ell_{\text{ext}}(\beta),$$ where both likelihoods are the conditional (partial) log-likelihoods of the respective matched datasets, with internal and external risk sets kept separated.

See also

cox_indi for the core function documentation.