Matched Versus Unmatched Analysis of Matched Case-Control Studies

Although the need for addressing matching in the analysis of matched case-control studies is well established, debate remains as to the most appropriate analytical method when matching on at least 1 continuous factor. We compared the bias and efficiency of unadjusted and adjusted conditional logistic regression (CLR) and unconditional logistic regression (ULR) in the setting of both exact and nonexact matching. To demonstrate that case-control matching distorts the association between the matching variables and the outcome in the matched sample relative to the target population, we derived the logit model for the matched case-control sample under exact matching. We conducted simulations to validate our theoretical conclusions and to explore different ways of adjusting for the matching variables in CLR and ULR to reduce biases. When matching is exact, CLR is unbiased in all settings. When matching is not exact, unadjusted CLR tends to be biased, and this bias increases with increasing matching caliper size. Spline smoothing of the matching variables in CLR can alleviate biases. Regardless of exact or nonexact matching, adjusted ULR is generally biased unless the functional form of the matched factors is modeled correctly. The validity of adjusted ULR is vulnerable to model specification error. CLR should remain the primary analytical approach.

Automated summary

This study compared different analytical methods in matched case-control studies and found that CLR is unbiased in exact matching, while unadjusted CLR tends to be biased in nonexact matching, but bias can be alleviated by spline smoothing of matching variables. Adjusted ULR generally has biases and is sensitive to model specification errors, highlighting the importance of using CLR as the primary analytical approach.