Bayesian analysis of pair‐matched case‐control studies subject to outcome misclassification

We examine the impact of nondifferential outcome misclassification on odds ratios estimated from pair‐matched case‐control studies and propose a Bayesian model to adjust these estimates for misclassification bias. The model relies on access to a validation subgroup with confirmed outcome status for...

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Bibliographic Details
Published inStatistics in medicine Vol. 36; no. 26; pp. 4196 - 4213
Main Authors Högg, Tanja, Petkau, John, Zhao, Yinshan, Gustafson, Paul, Wijnands, José MA, Tremlett, Helen
Format Journal Article
LanguageEnglish
Published England Wiley Subscription Services, Inc 20.11.2017
Subjects
Bayes Theorem
Bayesian analysis
Bayesian method
Bias
British Columbia
Case-Control Studies
Comorbidity
Computer Simulation
Data Interpretation, Statistical
Databases, Factual
health administrative databases
Humans
matched case‐control study
Morbidity
Multiple sclerosis
Multiple Sclerosis - complications
Odds Ratio
outcome misclassification
matched case-control study
odds ratio
Bayesian method
health administrative databases
outcome misclassification
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Summary:We examine the impact of nondifferential outcome misclassification on odds ratios estimated from pair‐matched case‐control studies and propose a Bayesian model to adjust these estimates for misclassification bias. The model relies on access to a validation subgroup with confirmed outcome status for all case‐control pairs as well as prior knowledge about the positive and negative predictive value of the classification mechanism. We illustrate the model's performance on simulated data and apply it to a database study examining the presence of ten morbidities in the prodromal phase of multiple sclerosis.
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SourceType-Scholarly Journals-1
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.7427