Use of causal inference methods in case-control studies: a methodology review

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.