A Bayesian approach for correcting exposure misclassification in meta-analysis

Research output: Contribution to journalArticle

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.

LanguageEnglish (US)
Pages115-130
Number of pages16
JournalStatistics in Medicine
Volume38
Issue number1
DOIs
StatePublished - Jan 15 2019

Fingerprint

Bayes Theorem
Misclassification
Bayesian Approach
Meta-Analysis
Observational Study
Observational Studies
Diabetic Neuropathies
Validation Studies
Randomized Controlled Trial
Peripheral Nervous System Diseases
Health Policy
Smoking
Random Effects Model
Randomized Controlled Trials
Outcome Assessment (Health Care)
Health
Valid
Interval
Model
Estimate

Keywords

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

PubMed: MeSH publication types

  • Journal Article

Cite this

A Bayesian approach for correcting exposure misclassification in meta-analysis. / Lian, Qinshu; Hodges, James S; Maclehose, Richard F; Chu, Haitao.

In: Statistics in Medicine, Vol. 38, No. 1, 15.01.2019, p. 115-130.

Research output: Contribution to journalArticle

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