A Bayesian approach for correcting exposure misclassification in meta-analysis

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 and an outcome (main studies), and the other on the association between the misclassified exposure and the true exposure (validation studies). We extend the current scope for using external validation data by relaxing the “transportability” assumption by means of random effects models. Our model accounts for heterogeneity between studies and can be extended to allow different studies to have different exposure measurements. The proposed model is evaluated through simulations and illustrated using real data from a meta-analysis of the effect of cigarette smoking on diabetic peripheral neuropathy.

Original languageEnglish (US)
Pages (from-to)115-130
Number of pages16
JournalStatistics in Medicine
Volume38
Issue number1
DOIs
StatePublished - Jan 15 2019

Bibliographical note

Funding Information:
We would like to thank James D. Neaton for helpful discussion, which has improved the presentation of this work. The research reported in this publication was supported in part by National Institute of Dental and Craniofacial Research R03DE024750 (H.C.), U.S. National Library of Medicine R21LM012197 (H.C.) and R21LM012744 (H.C., J.H.), National Institute of Diabetes and Digestive and Kidney Diseases U01DK106786 (H.C.), National Heart, Lung, and Blood Institute T32HL129956 (Q.L.). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health.

Publisher Copyright:
© 2018 John Wiley & Sons, Ltd.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • external validation data
  • meta-analysis
  • misclassification
  • observational studies

Fingerprint Dive into the research topics of 'A Bayesian approach for correcting exposure misclassification in meta-analysis'. Together they form a unique fingerprint.

Cite this