Conditional logistic regression for matched case-control studies — conditional_logit

Fits conditional logistic regression models for individually or frequency matched case-control data and returns publication-ready matched odds ratios.

Usage

conditional_logit_reg(
  data,
  outcome,
  exposures,
  matched_set,
  adjust_for = NULL,
  multivariable = FALSE,
  multivariate = NULL,
  model_stats = FALSE,
  format = c("flextable", "gt"),
  theme = "minimal",
  show_ref = TRUE
)

Arguments

data: A data frame containing the outcome, predictors, and matched-set identifier.
outcome: Binary case-control outcome. The second factor/character level is treated as the case; numeric outcomes must use 0/1 or 1/2 coding.
exposures: Predictor names. Quoted and bare names are accepted.
matched_set: Variable identifying each matched case-control set.
adjust_for: Optional covariates. In exposure-by-exposure mode, each exposure is fitted with these covariates.
multivariable: Logical. If TRUE, one model includes all exposures and optional adjustment variables.
multivariate: Optional alias for multivariable.
model_stats: Logical. If TRUE, return model N, case and control counts, matched-set count, AIC, and log-likelihood.
format: Output format: "flextable" (default) or "gt".
theme: Table theme preset.
show_ref: Logical. Display reference rows for categorical predictors.

Value

A gtregression object containing the publication table, underlying estimates, fitted clogit models, summaries, model statistics, and matching metadata.

Details

Matching is handled by conditioning on matched_set; no odds ratio is estimated for that variable. Matched sets without both a case and a control are not informative for conditional logistic regression and are excluded with a message. This function is intended for matched case-control studies, not complex survey analysis.

Examples

matched_data <- data.frame(
  set = rep(1:8, each = 3),
  case = rep(c(1, 0, 0), 8),
  exposed = factor(rep(c("Yes", "No", "No"), 8),
                   levels = c("No", "Yes")),
  age = rep(40:47, each = 3) + rep(c(0, -1, 1), 8)
)

conditional_logit_reg(
  matched_data,
  outcome = case,
  exposures = exposed,
  matched_set = set
)
#> Warning: Ran out of iterations and did not converge