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 analytic method when matching on at least one continuous factor. We compare the bias and efficiency of unadjusted and adjusted conditional logistic regression (CLR) and unconditional logistic regression (ULR) in the setting of both exact and non exact 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 derive the logit model for the matched case control sample under exact matching. We conduct 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 non exact matching, adjusted ULR is generally biased unless the functional form of the matched factors is modelled correctly. The validity of adjusted ULR is vulnerable to model specification error. CLR should remain the primary analytic approach.
American journal of epidemiology