Adjusting for outcome misclassification: the importance of accounting for case-control sampling and other forms of outcome-related selection

Purpose: Special care must be taken when adjusting for outcome misclassification in case-control data. Basic adjustment formulas using either sensitivity and specificity or predictive values (as with external validation data) do not account for the fact that controls are sampled from a much larger pool of potential controls. A parallel problem arises in surveys and cohort studies in which participation or loss is outcome related. Methods: We review this problem and provide simple methods to adjust for outcome misclassification in case-control studies, and illustrate the methods in a case-control birth certificate study of cleft lip/palate and maternal cigarette smoking during pregnancy. Results: Adjustment formulas for outcome misclassification that ignore case-control sampling can yield severely biased results. In the data we examined, the magnitude of error caused by not accounting for sampling is small when population sensitivity and specificity are high, but increases as (1) population sensitivity decreases, (2) population specificity decreases, and (3) the magnitude of the differentiality increases. Failing to account for case-control sampling can result in an odds ratio adjusted for outcome misclassification that is either too high or too low. Conclusions: One needs to account for outcome-related selection (such as case-control sampling) when adjusting for outcome misclassification using external information. (C) 2013 Elsevier Inc. All rights reserved.