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Bayesian analysis of pair‐matched case‐control studies subject to outcome misclassification
Author(s) -
Högg Tanja,
Petkau John,
Zhao Yinshan,
Gustafson Paul,
Wijnands José MA,
Tremlett Helen
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7427
Subject(s) - outcome (game theory) , bayesian probability , computer science , subject (documents) , statistics , econometrics , artificial intelligence , mathematics , mathematical economics , library science
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.