The comparison is complex in that the efficiency is affected not only by matching but also by other factors that are difficult to specify in advance ( 9).

Previous studies have compared efficiency of matched and unmatched studies ( 3, 6– 8). The study efficiency is improved if a smaller sample size is needed for the same precision or a narrower confidence interval is obtained using the same sample size. Other reasons to match include control of unmeasured confounders and ensuring statistical power to perform subgroup analysis and to test for interactions ( 5). Earlier literature often describes the advantages of matching in case–control studies as adjusting for confounding and improving the study efficiency ( 1– 4). It ensures that adjustment is possible when there is no sufficient overlap in confounding variables between cases and a random set of controls. Matching is commonly used in case–control studies to adjust for confounding at the design stage. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls. Our results support our hypothesis however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case–control data. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Conditional logistic regression has become a standard for matched case–control data to tackle the sparse data problem.

There is a presumption that matched data need to be analyzed by matched methods. Matching on demographic variables is commonly used in case–control studies to adjust for confounding at the design stage. 2Department of Community Medicine and Health Care, University of Connecticut Health Center, Farmington, CT, United States.1Connecticut Institute for Clinical and Translational Science, University of Connecticut Health Center, Farmington, CT, United States.

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