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Methods to calculate uncertainty in the estimated overall effect size from a random-effects meta-analysis.

Authors
  • Veroniki, Areti Angeliki1, 2
  • Jackson, Dan3
  • Bender, Ralf4
  • Kuss, Oliver5, 6
  • Langan, Dean7
  • Higgins, Julian P T8
  • Knapp, Guido9
  • Salanti, Georgia10
  • 1 Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada. , (Canada)
  • 2 Department of Primary Education, School of Education, University of Ioannina, Ioannina, Greece. , (Greece)
  • 3 MRC Biostatistics Unit, Statistical Innovation Group, AstraZeneca, Cambridge, UK.
  • 4 Department of Medical Biometry, Institute for Quality and Efficiency in Health Care, Cologne, Germany. , (Germany)
  • 5 Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany. , (Germany)
  • 6 Institute of Medical Statistics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. , (Germany)
  • 7 Institute of Child Health, University College London, London, UK.
  • 8 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
  • 9 Department of Statistics, TU Dortmund University, Dortmund, Germany. , (Germany)
  • 10 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. , (Switzerland)
Type
Published Article
Journal
Research synthesis methods
Publication Date
Mar 01, 2019
Volume
10
Issue
1
Pages
23–43
Identifiers
DOI: 10.1002/jrsm.1319
PMID: 30129707
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Meta-analyses are an important tool within systematic reviews to estimate the overall effect size and its confidence interval for an outcome of interest. If heterogeneity between the results of the relevant studies is anticipated, then a random-effects model is often preferred for analysis. In this model, a prediction interval for the true effect in a new study also provides additional useful information. However, the DerSimonian and Laird method-frequently used as the default method for meta-analyses with random effects-has been long challenged due to its unfavorable statistical properties. Several alternative methods have been proposed that may have better statistical properties in specific scenarios. In this paper, we aim to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random-effects model. We indicate whether some methods are preferable than others by considering the results of comparative simulation and real-life data studies. © 2018 John Wiley & Sons, Ltd.

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