A Bayesian Approach for Correcting Exposure Misclassification in Meta-Analysis

Details

• Personal Author:
  Chu H ; Hodges JS ; Lian Q ; MacLehose R
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Author Keywords: External Validation Data Data Processing Disease Incidence Mathematical Models Meta-analysis Misclassification Observational Studies Statistical Analysis Statistical Quality Control
• ISSN:
  0277-6715
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Minnesota ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  115-130
• Volume:
  38
• Issue:
  1
• NIOSHTIC Number:
  nn:20064450
• Citation:
  Stat Med 2019 Jan; 38(1):115-130
• Contact Point Address:
  Qinshu Lian, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455
• Email:
  lianx025@umn.edu
• Federal Fiscal Year:
  2019
• Performing Organization:
  University of Minnesota Twin Cities
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Statistics in Medicine
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:ce2832a00bdd3f104054029f0a1db64aef4e321956f072b8a6efa30fa400a91bac9e9b4c306b11c740a10d34d1c80ea4c96c27f45cd81b8126ea9cde4c051991
• Download URL:
  https://stacks.cdc.gov/view/cdc/222906/cdc_222906_DS1.pdf
• File Type:
  [PDF - 759.88 KB ]