Inverse probability weighting in nested case‐control studies with additional matching—a simulation study

Nested case-control designs are inevitably less efficient than full cohort designs, and it is important to use available information as efficiently as possible. Reuse of controls by inverse probability weighting may be one way to obtain efficiency improvements, and it can be particularly advantageous when two or more endpoints are analyzed in the same cohort. The controls in a nested case-control design are often matched on additional factors than at risk status, and this should be taken into account when reusing controls. Although some studies have suggested methods for handling additional matching, a thorough investigation of how this affects parameter estimates and weights is lacking. Our aim is to provide such a discussion to help developing guidelines for practitioners. We demonstrate that it is important to adjust for the matching variables in regression analyses when the matching is broken. We present three types of estimators for the inverse sampling probabilities accounting for additional matching. One of these estimators was somewhat biased when the cases and controls were matched very closely. We investigated how additional matching affected estimates of interest, with varying degree of association between the matching variables and exposure/outcome. Strong associations introduced only a small bias when the matching variables were properly adjusted for. Sometimes, exposure variables, for example, blood samples, are analyzed in batches. Rather, strong batch effects had to be present before this introduced much bias when the matching was broken. All simulations are based on a study of prostate cancer and vitamin D.