Misclassification of outcome in case–control studies: Methods for sensitivity analysis. (October 2016)

• Record Type: Journal Article
• Authors: Gilbert, Rebecca; Martin, Richard M; Donovan, Jenny; Lane, J Athene; Hamdy, Freddie; Neal, David E; Metcalfe, Chris
• Is Part Of: Statistical methods in medical research. Volume 25:Number 5(2016:Oct.)
• Page Start: 2377
• Page End: 2393
• Publication Date: 2016-10
• Subjects: misclassification of outcome -- case–control study -- risk factors for prostate cancer; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280214523192
• Languages: English
• ISSNs: 0962-2802

Abstract

Case–control studies are potentially open to misclassification of disease outcome which may be unrelated to risk factor exposure (non-differential), thus underestimating associations, or related to risk factor exposure (differential), thus causing more serious bias. We conducted a systematic literature review for methods of adjusting for outcome misclassification in case–control studies. We also applied methods to simulated data with known outcome misclassification to assess performance of these methods. Finally, real data from the Prostate Testing for Cancer and Treatment (ProtecT) randomised controlled trial gauged the usefulness of these methods. Adjustment methods range from recalculating cell frequencies to probabilistic sensitivity modelling and Bayesian models, which incorporate uncertainty in sensitivity and specificity estimates. Simulated data indicated that substantial bias in either direction resulted from differential misclassification. More sophisticated methods, incorporating uncertainty into estimates of misclassification, provided appropriately wide confidence intervals for corrected estimates of risk factor–disease association. Method choice depends on whether the objective is to assess if an observed association can be explained by bias, or to provide a 'corrected' estimate for the primary analysis. Accurate estimation of the degree of misclassification is important for the latter; otherwise further bias may be introduced.