Bayesian adjustment for exposure misclassification in case-control studies.
Poor measurement of explanatory variables occurs frequently in observational studies. Error-prone observations may lead to biased estimation and loss of power in detecting the impact of explanatory variables on the response. We consider misclassified binary exposure in the context of case-control studies, assuming the availability of validation data to inform the magnitude of the misclassification. A Bayesian adjustment to correct the misclassification is investigated. Simulation studies show that the Bayesian method can have advantages over non-Bayesian counterparts, particularly in the face of a rare exposure, small validation sample sizes, and uncertainty about whether exposure misclassification is differential or non-differential. The method is illustrated via application to several real studies.
Chu
Rong
R
Department of Clinical Epidemiology and Biostatistics, McMaster University, Canada. chur@mcmaster.ca
Gustafson
Paul
P
Le
Nhu
N
D000900:Anti-Bacterial Agents / Q000009:adverse effects; D001499:Bayes Theorem; D056808:Biostatistics; D016022:Case-Control Studies; D003198:Computer Simulation; D005260:Female; D018258:Herpesvirus 2, Human / Q000472:pathogenicity; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016017:Odds Ratio; D011247:Pregnancy; D011297:Prenatal Exposure Delayed Effects; D018401:Sample Size; D013398:Sudden Infant Death / Q000209:etiology; D002583:Uterine Cervical Neoplasms / Q000209:etiology
D000900:Anti-Bacterial Agents