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A re-evaluation of random-effects meta-analysis

Author

Listed:
  • Julian P. T. Higgins
  • Simon G. Thompson
  • David J. Spiegelhalter

Abstract

Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to "a priori" judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders. Copyright Journal compilation (c) 2009 Royal Statistical Society.

Suggested Citation

  • Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re-evaluation of random-effects meta-analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159.
  • Handle: RePEc:bla:jorssa:v:172:y:2009:i:1:p:137-159
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-985X.2008.00552.x
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    Citations

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    Cited by:

    1. Ian R. White, 2011. "Multivariate random-effects meta-regression: Updates to mvmeta," Stata Journal, StataCorp LP, vol. 11(2), pages 255-270, June.
    2. Tomáš Havránek, 2009. "Rose Effect and the Euro: The Magic is Gone," Working Papers IES 2009/20, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2009.
    3. Yeojin Chung & Sophia Rabe-Hesketh & Vincent Dorie & Andrew Gelman & Jingchen Liu, 2013. "A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 685-709, October.
    4. repec:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0728-0 is not listed on IDEAS
    5. repec:eee:csdana:v:113:y:2017:i:c:p:100-110 is not listed on IDEAS
    6. Yip, Paul S.F. & Yousuf, Saman & Chan, Chee Hon & Yung, Tiffany & Wu, Kevin C.-C., 2015. "The roles of culture and gender in the relationship between divorce and suicide risk: A meta-analysis," Social Science & Medicine, Elsevier, vol. 128(C), pages 87-94.
    7. Takahiro Hasegawa & Brian Claggett & Lu Tian & Scott D. Solomon & Marc A. Pfeffer & Lee-Jen Wei, 0. "The Myth of Making Inferences for an Overall Treatment Efficacy with Data from Multiple Comparative Studies Via Meta-Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 0, pages 1-14.
    8. repec:spr:psycho:v:82:y:2017:i:2:d:10.1007_s11336-016-9507-z is not listed on IDEAS
    9. repec:spr:stabio:v:9:y:2017:i:1:d:10.1007_s12561-016-9179-3 is not listed on IDEAS

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