A simple Bayesian analysis of misclassified binary data with a validation substudy.
A two-stage Bayesian method is presented for analyzing case-control studies in which a binary variable is sometimes measured with error but the correct values of the variable are known for a random subset of the study group. The first stage of the method is analytically tractable and MCMC methods are used for the second stage. The posterior distribution from the first stage becomes the prior distribution for the second stage, thus transferring all relevant information between the stages. The method makes few distributional assumptions and requires no asymptotic approximations. It is computationally fast and can be run using standard software. It is applied to two data sets that have been analyzed by other methods, and results are compared.
Prescott
Gordon J
GJ
Department of Public Health, University of Aberdeen, UK.
Garthwaite
Paul H
PH
D000900:Anti-Bacterial Agents / Q000009:adverse effects; D001499:Bayes Theorem; D001699:Biometry; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007223:Infant; D008390:Markov Chains; D008431:Maternal-Fetal Exchange; D009010:Monte Carlo Method; D016017:Odds Ratio; D011247:Pregnancy; D018139:Simplexvirus / Q000472:pathogenicity; D013398:Sudden Infant Death / Q000209:etiology; D002583:Uterine Cervical Neoplasms / Q000209:etiology
D000900:Anti-Bacterial Agents