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  "path": "/t/is-the-use-of-conditional-logistic-regression-necessary-in-case-control-study/28674#post_2",
  "publishedAt": "2026-03-25T11:27:00.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "There is an old literature about this which was fairly well captured in the above reference. I asked Norm Breslow about it at a seminar he presented at UNC when I was a student, and his reply was clear: If your model is saturated, e.g., you model age with many nonlinear terms, you’ll get the same inference for the exposure from the conventional logistic model as you do from the conditional one. So the conditional model is best used when the adjustment factors are hard to model, e.g., you need to match on occupation (and their are hundreds on them in the data) or you’re doing studies on twins.\n\nThere are lessons to be learned from propensity matching, where researchers have fallen into the bad habit of assuming that you don’t need to adjust for prognostic factors outside of the matching (pure confounding adjustment).",
  "title": "Is the use of conditional logistic regression necessary in case-control study？"
}