Impact of and Correction for Outcome Misclassification in Cumulative Incidence Estimation

PloS One
Giorgos Bakoyannis, Constantin T Yiannoutsos

Abstract

Cohort studies and clinical trials may involve multiple events. When occurrence of one of these events prevents the observance of another, the situation is called "competing risks". A useful measure in such studies is the cumulative incidence of an event, which is useful in evaluating interventions or assessing disease prognosis. When outcomes in such studies are subject to misclassification, the resulting cumulative incidence estimates may be biased. In this work, we study the mechanism of bias in cumulative incidence estimation due to outcome misclassification. We show that even moderate levels of misclassification can lead to seriously biased estimates in a frequently unpredictable manner. We propose an easy to use estimator for correcting this bias that is uniformly consistent. Extensive simulations suggest that this method leads to unbiased estimates in practical settings. The proposed method is useful, both in settings where misclassification probabilities are known by historical data or can be estimated by other means, and for performing sensitivity analyses when the misclassification probabilities are not precisely known.

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Citations

Jul 30, 2016·American Journal of Epidemiology·Jessie K EdwardsDaniel Westreich
May 14, 2019·Clinical Trials : Journal of the Society for Clinical Trials·Qinli MaKevin Haynes
Apr 19, 2019·The Journal of Infectious Diseases·McKaylee M RobertsonDenis Nash
Nov 19, 2019·Family Practice·Anh PhamTyler Williamson
Aug 22, 2017·Current Epidemiology Reports·Jessie K EdwardsCatherine R Lesko
Feb 9, 2018·American Journal of Epidemiology·Jessie K EdwardsJoseph J Eron
Jul 20, 2019·Lung Cancer : Journal of the International Association for the Study of Lung Cancer·Kay See Tan
Jun 12, 2021·Statistical Communications in Infectious Diseases·Giorgos BakoyannisConstantin T Yiannoutsos

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