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Cross-Validated CLR with Individual-Level External Data

cv.ncc_indi.Rd

Performs K-fold cross-validation (CV) to select the integration parameter eta for Conditional Logistic Regression with individual-level external data integration, implemented via ncc_indi.

This function is designed for 1:m matched case-control settings where each stratum (matched set) contains exactly one case and \(m\) controls.

Usage

cv.ncc_indi(
  y_int,
  z_int,
  stratum_int,
  y_ext,
  z_ext,
  stratum_ext,
  etas = NULL,
  nfolds = 5,
  criteria = c("loss", "AUC", "CIndex", "Brier"),
  max_iter = 100,
  tol = 1e-07,
  message = FALSE,
  seed = NULL
)

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 internal matched sets. 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.

stratum_ext

Numeric or factor vector defining the external matched sets. Required.

etas

Numeric vector of candidate tuning values for \(\eta\). Required.

nfolds

Number of cross-validation folds. Default 5.

criteria

Character string specifying the CV performance criterion. One of "loss" (default), "AUC", "CIndex", or "Brier".

max_iter

Maximum number of Newton-Raphson iterations passed to ncc_indi. Default 100.

tol

Convergence tolerance passed to ncc_indi. Default 1e-7.

message

Logical. If TRUE, prints progress messages. Default FALSE.

seed

Optional integer seed for reproducible fold assignment. Default NULL.

Value

A list of class "cv.ncc_indi" containing:

internal_stat

A data.frame with one row per eta and the CV metric for the chosen criteria.

beta_full

Matrix of coefficients from the full-data fit (columns correspond to etas).

best

A list with best_eta, best_beta, and criteria.

criteria

The criterion used for selection.

nfolds

The number of folds used.

Details

Cross-validation is performed at the stratum level on the internal dataset: each matched set is treated as an indivisible unit and assigned to a single fold using get_fold_cc. The external dataset is used in full during every training fold.

The criteria argument controls the CV performance metric:

  • "loss": Average negative conditional log-likelihood on held-out strata.

  • "AUC": Matched-set AUC based on within-stratum comparisons.

  • "CIndex": Alias for "AUC" in the 1:m matched setting.

  • "Brier": Conditional Brier score based on within-stratum softmax probabilities.

See also

ncc_indi, cv.ncckl