Comparison of the Missing-Indicator Method and Conditional Logistic Regression in 1:m Matched Case-Control Studies with Missing Exposure Values

The missing-indicator method and conditional logistic regression have been recommended as alternative approaches for data analysis in matched case-control studies with missing exposure values. The authors evaluated the performance of the two methods using Monte Carlo simulation. Data were generated from a 1:m matched design based on McNemar's 2 x 2 tables with four scenarios for missing values: completely-at-random, case-dependent, exposure-dependent, and case/exposure-dependent. In their analysis, the authors used conditional logistic regression for complete pairs and the missing-indicator method for all pairs. For 1:1 matched studies, given no confounding between exposure and disease, the two methods yielded unbiased estimates. Otherwise, conditional logistic regression produced unbiased estimates with empirical confidence interval coverage similar to nominal coverage under the first three missing-value scenarios, whereas the missing-indicator method produced slightly more bias and lower confidence interval coverage. An increased number of matched controls was associated with slightly more bias and lower confidence interval coverage. Under the case/ exposure-dependent missing-value scenario, neither method performed satisfactorily; this indicates the need for more sophisticated statistical methods for handling such missing values. Overall, compared with the missing-indicator method, conditional logistic regression provided a slight advantage in terms of bias and coverage probability, at the cost of slightly reduced statistical power and efficiency.