Meta-Analysis of Published Excess Relative Risk Estimates

Details

• Personal Author:
  Abalo K ; Bernier M-O ; Keil AP ; Laurier D ; Leuraud K ; Little MP ; Rage E ; Richardson DB
• Description:
  A meta-analytic summary effect estimate often is calculated as an inverse-variance-weighted average of study-specific estimates of association. The variances of published estimates of association often are derived from their associated confidence intervals under assumptions typical of Wald-type statistics, such as normality of the parameter. However, in some research areas, such as radiation epidemiology, epidemiological results typically are obtained by fitting linear relative risk models, and associated likelihood-based confidence intervals are often asymmetric; consequently, reasonable estimates of variances associated with study-specific estimates of association may be difficult to infer from the standard approach based on the assumption of a Wald-type interval. Here, a novel method is described for meta-analysis of published results from linear relative risk models that uses a parametric transformation of published results to improve on the normal approximation used to assess confidence intervals. Using simulations, it is illustrated that the meta-analytic summary obtained using the proposed approach yields less biased summary estimates, with better confidence interval coverage, than the summary obtained using the more classical approach to meta-analysis. The proposed approach is illustrated using a previously published example of meta-analysis of epidemiological findings regarding circulatory disease following exposure to low-level ionizing radiation. [Description provided by NIOSH]
• Subjects:
  Cohort Studies Epidemiology Neoplasms Occupational Health Safety
• Keywords:
  Author Keywords: Cancer Cancer Circulatory System Cohort Studies Excess Relative Risk Ionizing Radiation Meta-analysis Models Risk Assessment
• ISSN:
  0301-634X
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Maryland ; North Carolina ; OSHA Region 3 ; OSHA Region 4 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  59
• Issue:
  4
• NIOSHTIC Number:
  nn:20060488
• Citation:
  Radiat Environ Biophys 2020 Nov; 59(4):631-641
• Contact Point Address:
  David B. Richardson, Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
• Email:
  david.richardson@unc.edu
• Federal Fiscal Year:
  2021
• Performing Organization:
  University of North Carolina, Chapel Hill
• Peer Reviewed:
  True
• Start Date:
  20150901
• Source Full Name:
  Radiation and Environmental Biophysics
• End Date:
  20170831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:ef84e3d7367e8d19135cdd106578ed8b1724300a342be6516c932daa5b87c9b56f1bc0eebae20083bba09f47991eb014e84976556f47aef3d3d3153a2fe61385
• Download URL:
  https://stacks.cdc.gov/view/cdc/224363/cdc_224363_DS1.pdf
• File Type:
  [PDF - 864.25 KB ]