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Assessing Heterogeneity in Random-Effects Meta-analysis

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Meta-Research

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2345))

Abstract

The random-effects model allows for the possibility that studies in a meta-analysis have heterogeneous effects. That is, observed study estimates vary not only due to random sampling error but also due to inherent differences in the way studies have been designed and conducted. In this chapter, we consider methods to estimate the heterogeneity variance parameter in a random-effects model, consider in more detail what this parameter represents and how the possible explanations for heterogeneity can be explored through statistical methods. Toward the end of this chapter, publication bias is discussed as an alternative explanation for why observed effect estimates might form some distribution other than what we might come to expect.

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References

  1. Borenstein M, Hedges LV, Higgins JPT (2009) Introduction to meta-analysis. Wiley, Hoboken, NJ

    Book  Google Scholar 

  2. Higgins JPT, Thompson SG, Spiegelhalter DJ (2009) A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc 172(1):137–159

    Article  Google Scholar 

  3. Deeks JJ, Higgins JPT, Altman DG (2011) Chapter 9: analysing data and undertaking meta-analyses. In: Higgins JPT, Green S (eds) Cochrane handbook for systematic reviews of interventions version 5.1.0 (updated March 2011). The Cochrane Collaboration, London. www.handbook.cochrane.org

    Google Scholar 

  4. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327(7414):557–560

    Article  Google Scholar 

  5. Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 1(2):97–111

    Article  Google Scholar 

  6. Veroniki AA, Jackson D, Viechtbauer W et al (2016) Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods 7(1):55–79

    Article  Google Scholar 

  7. Langan D, Higgins JP, Simmonds M (2017) Comparative performance of heterogeneity variance estimators in meta-analysis: a review of simulation studies. Res Synth Methods 8(2):181–198. https://doi.org/10.1002/jrsm.1198

    Article  PubMed  Google Scholar 

  8. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7(3):177–188

    Article  CAS  Google Scholar 

  9. Paule R, Mandel J (1982) Consensus values and weighting factors. J Res Natl Bur Stand 87(5):377–385

    Article  Google Scholar 

  10. Harville DA (1977) Maximum likelihood approaches to variance component estimation and to related problems. J Am Stat Assoc 72(358):320–338

    Article  Google Scholar 

  11. Langan D, Higgins JP, Jackson D et al (2019) A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses. Res Synth Methods 10(1):83–98. https://doi.org/10.1002/jrsm.1316

    Article  PubMed  Google Scholar 

  12. DerSimonian R, Kacker R (2007) Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials 28(2):105–114

    Article  Google Scholar 

  13. Bowden J, Tierney J, Copas A, Burdett S (2011) Quantifying, displaying and accounting for heterogeneity in the meta-analysis of RCTs using standard and generalised Q statistics. BMC Med Res Methodol 11(1):41

    Article  Google Scholar 

  14. Rukhin AL, Biggerstaff BJ, Vangel MG (2000) Restricted maximum likelihood estimation of a common mean and the Mandel-Paule algorithm. J Stat Plan Infer 83(2):319–330

    Article  Google Scholar 

  15. Hardy RJ, Thompson SG (1996) A likelihood approach to meta-analysis with random effects. Stat Med 15(6):619–629

    Article  CAS  Google Scholar 

  16. Hartung J, Makambi KH (2003) Reducing the number of unjustified significant results in meta-analysis. Commun Stat Simul Comput 32(4):1179–1190

    Article  Google Scholar 

  17. Viechtbauer W (2005) Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Stat 30(3):261–293

    Article  Google Scholar 

  18. Jennrich RI, Sampson P (1976) Newton-Raphson and related algorithms for maximum likelihood variance component estimation. Technometrics 18(1):11–17

    Article  Google Scholar 

  19. Kontopantelis E, Springate DA, Reeves D (2013) A re-analysis of the Cochrane library data: the dangers of unobserved heterogeneity in meta-analyses. PLoS One 8:7

    Article  Google Scholar 

  20. Higgins JPT, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558

    Article  Google Scholar 

  21. Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JP (2012) Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol 41(3):818–827

    Article  Google Scholar 

  22. Davey J, Turner RM, Clarke MJ, Higgins JP (2011) Characteristics of meta-analyses and their component studies in the Cochrane Database of Systematic Reviews: a cross-sectional, descriptive analysis. BMC Med Res Methodol 11:160

    Article  Google Scholar 

  23. Novianti PW, Roes KC, van der Tweel I (2014) Estimation of between-trial variance in sequential meta-analyses: a simulation study. Contemp Clin Trials 37(1):129–138

    Article  Google Scholar 

  24. Bhaumik DK, Amatya A, Normand S-LT, Greenhouse J, Kaizar E, Neelon B, Gibbons RD (2012) Meta-analysis of rare binary adverse event data. J Am Stat Assoc 107(498):555–567

    Article  Google Scholar 

  25. Panityakul T, Bumrungsup C, Knapp G (2013) On estimating residual heterogeneity in random-effects meta-regression: a comparative study. J Stat Theory Appl 12(3):253–265

    Google Scholar 

  26. Böhning D, Malzahn U, Dietz E, Schlattmann P, Viwat-wongkasem C, Biggeri A (2002) Some general points in estimating heterogeneity variance with the DerSimonian-Laird estimator. Biostatistics 3(4):445–457

    Article  Google Scholar 

  27. Katalinic OM, Harvey LA, Herbert RD, Moseley AM, Lannin NA, Schurr K (2010) Stretch for the treatment and prevention of contractures. Cochrane Database Syst Rev (9)

    Google Scholar 

  28. Polanczyk GV, Willcutt EG, Salum GA, Kieling C, Rohde LA (2014) ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. Int J Epidemiol 43(2):434–442

    Article  Google Scholar 

  29. Higgins JP, Spiegelhalter DJ (2002) Being sceptical about meta-analyses: a Bayesian perspective on magnesium trials in myocardial infarction. Int J Epidemiol 31(1):96–104

    Article  Google Scholar 

  30. Doucouliagos H, Ulubaşoğlu MA (2008) Democracy and economic growth: a meta-analysis. Am J Polit Sci 52(1):61–83

    Article  Google Scholar 

  31. Ladeiras-Lopes R, Pereira AK, Nogueira A, Pinheiro-Torres T, Pinto I, Santos-Pereira R, Lunet N (2008) Smoking and gastric cancer: systematic review and meta-analysis of cohort studies. Cancer Causes Control 19(7):689–701

    Article  Google Scholar 

  32. Shi L, Lin L (2019) The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses. Medicine 98(23):e15987

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Applied Statistics Courses, UCL Great Ormond Street Institute of Child Health, London, UK

    Dean Langan

Authors
  1. Dean Langan
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Correspondence to Dean Langan .

Editor information

Editors and Affiliations

  1. Department of Hygiene and Epidemiology University of Ioannina Medical School, Department of Epidemiology and Biostatistics Imperial College London London, UK, Ioannina, Greece

    Evangelos Evangelou

  2. Knowledge Translation Program Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Department of Surgery and Cancer Faculty of Medicine Institute of Reproductive and Developmental Biology Imperial College London, UK ISSN, Toronto, ON, Canada

    Areti Angeliki Veroniki

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Langan, D. (2022). Assessing Heterogeneity in Random-Effects Meta-analysis. In: Evangelou, E., Veroniki, A.A. (eds) Meta-Research. Methods in Molecular Biology, vol 2345. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1566-9_4

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  • DOI: https://doi.org/10.1007/978-1-0716-1566-9_4

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1565-2

  • Online ISBN: 978-1-0716-1566-9

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