Matched cohort methods for injury research.

This article reviews the design and analysis of matched cohort studies of injuries where exposed study subjects are matched to others not exposed. We focus on the situation in which data are available for the matched groups with at least one member who had the study outcome, but data are absent or incomplete for matched groups that have no members with the outcome. When matching is done in a case-control study, those with the outcome are matched to those without the outcome on certain confounder measures; this distorts the exposure status of the controls to be like that of the cases in regard to the matching variables (and perhaps other variables as well) (1). As a consequence, the selected controls may not represent the exposure experience of the entire population from which the cases were derived. Therefore, matching is a source of selection bias in a case-control study. The bias it produces can be removed in the analysis by accounting for the matching since, conditional on the values of the matching variables, controls will be representative of the source population. The consequence of one-to-one matching in a cohort study is different. A variable can be a confounder only if, in the study cohort, it is associated with but not affected by the exposure and is independently predictive of the outcome. If each exposed study subject is perfectly matched to an unexposed subject on the value of some variable, and if there is no subject loss or missing data, there will be no association of exposure with the matching variable in the data, and confounding by the matching variable will be eliminated (2). Confounding by the matching variable could still occur, however, if an imbalance arose between the exposed and unexposed study subjects; this might happen, for example, if follow-up were less complete for one group compared with the other, or if some records were omitted from the analysis because of missing data. Despite the potential of matching to prevent confounding in a cohort study and the potential of matching to sometimes increase study efficiency (2), it appears that this design is rarely used. For many cohort studies, matching exposed persons to one or several unexposed persons would be laborious. Furthermore, it might be wasteful in that matches might be unavailable for some potential cohort members. Today, cohort studies usually avoid matching, and the data are analyzed using regression methods that make it relatively easy to adjust for potential confounding factors that might otherwise be used for matching.