Effect heterogeneity by a matching covariate in matched case-control studies: a method for graphs-based representation.

This research devises a methodology for assessing and depicting effect heterogeneity associated with a matching covariate in case-control studies, as demonstrated in a case-control study involving horses with colic. The method is ideal for interpreting varying patterns across ordered categorical or continuous covariates, and is applicable even when assuming causality.

Objectives and Method of the Study

• The primary aim of the study is to formulate a method that can effectively assess and characterize effect heterogeneity based on a matching covariate in case-control studies. Effect heterogeneity refers to the phenomenon of the treatment effect varying across levels of another variable (the matching covariate).
• The methodology is illustrated using a case-control study conducted on horses with colic in Texas between 1997 and 1998.
• The researchers matched horses by the treating veterinarian and the month of examination, ultimately resulting in 498 pairs of cases-controls.

Demonstration of the Method

• The authors reveal that there is effect heterogeneity in relation to one specific characteristic – cluster size of veterinarians, which impacts colic’s association with certain covariates.
• This effect heterogeneity is demonstrated using a moving average approach to conditional logistic regression and graphs-based techniques.

Applicability of the Method

• The proposed approach can be applied to investigate effect heterogeneity, or effect modification, by any ordered categorical or continuous covariates for which cases and controls have been matched.
• The introduced method can depict the variance pattern across ordered categorical or continuous matching covariates, and it can accommodate any shape for this pattern.
• The method is applicable even in instances where causality might be justifiably assumed.