A Bayesian approach for correcting exposure misclassification in meta-analysis.

ABSTRACT: In observational studies, misclassification of exposure is ubiquitous and can substantially bias the estimated association between an outcome and an exposure. Although misclassification in a single observational study has been well studied, few papers have considered it in a meta-analysis. Meta-analyses of observational studies provide important evidence for health policy decisions, especially when large randomized controlled trials are unethical or unavailable. It is imperative to account properly for misclassification in a meta-analysis to obtain valid point and interval estimates. In this paper, we propose a novel Bayesian approach to filling this methodological gap. We simultaneously synthesize two (or more) meta-analyses, with one on the association between a misclassified exposure an

SUBMITTER: Lian Q 

PROVIDER: S-EPMC6779051 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature