Use of causal inference methods in case–control studies: a methodology review
Bibliographic record
Abstract
The use of causal inference methods in cohort studies has increased considerably in recent years. However, their use has been limited in case-control studies. This report aimed at providing a detailed review of causal inference methods used in case-control studies and to review and examine their applications in previous studies. Several methods have been used to facilitate causal inference in case-control studies, including intercept-adjustment, propensity scores, and weight-based and doubly robust estimators. We used the Medical Literature Analysis and Retrieval System Online database to identify original peer-reviewed case-control studies conducted from March 2014 to March 2024 that applied these methods. We identified 418 studies, 23 of which met the inclusion criteria. Most studies involved case-control matching (individual or frequency) and included incident cases. The covariate-conditional odds ratio was the most frequently reported estimated parameter. Sixty-five percent of included studies considered an adjustment for sampling bias, most often using inverse-probability of observation weighting and case-control targeted maximum likelihood approaches. We are still in the early stages of development and application of causal inference methods for case-control studies. Their implementation and new techniques to address time-varying confounding can improve the validity of study findings and should be encouraged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".