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Combining Statistical Evidence


  • Elena Kulinskaya
  • Stephan Morgenthaler
  • Robert G. Staudte


type="main" xml:id="insr12037-abs-0001"> The combination of evidence from independent studies has a curious history. The origins reach back at least to the beginning of the 20th century. Since the mid-1970s, meta-analysis has become popular in several fields, among them medical statistics and the behavioural sciences. The most widely used procedures were perfected in early papers, and subsequently, a kind of groupthink has taken hold of meta-analysis. This explains the need for a review in a statistics journal, destined for a statistical audience. Meta-analysis is not a hot research topic among graduate students in statistics, and by writing this article, we hope to change this. We wish to point out the shortcomings of the mainstream view and exhibit some of the open problems that await the attention of statistical researchers. A host of competent reviews of meta-analysis have been published, and several book-length treatments are also available. We have listed many of these in the bibliography but cannot guarantee completeness.

Suggested Citation

  • Elena Kulinskaya & Stephan Morgenthaler & Robert G. Staudte, 2014. "Combining Statistical Evidence," International Statistical Review, International Statistical Institute, vol. 82(2), pages 214-242, August.
  • Handle: RePEc:bla:istatr:v:82:y:2014:i:2:p:214-242

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    References listed on IDEAS

    1. Han Chen & Alisa K. Manning & Josée Dupuis, 2012. "A Method of Moments Estimator for Random Effect Multivariate Meta-Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1278-1284, December.
    2. Ian R. White, 2009. "Multivariate random-effects meta-analysis," Stata Journal, StataCorp LP, vol. 9(1), pages 40-56, March.
    3. Xie, Minge & Singh, Kesar & Strawderman, William E., 2011. "Confidence Distributions and a Unifying Framework for Meta-Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 320-333.
    4. Andrew L. Rukhin, 2013. "Estimating heterogeneity variance in meta-analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 451-469, June.
    5. Sidik, Kurex & Jonkman, Jeffrey N., 2006. "Robust variance estimation for random effects meta-analysis," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3681-3701, August.
    6. Chang, Ching-Hui & Pal, Nabendu, 2008. "Testing on the common mean of several normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 321-333, December.
    7. Thomas Mathew & Kenneth Nordstrom, 1999. "On the Equivalence of Meta-Analysis Using Literature and Using Individual Patient Data," Biometrics, The International Biometric Society, vol. 55(4), pages 1221-1223, December.
    8. Sue Duval & Richard Tweedie, 2000. "Trim and Fill: A Simple Funnel-Plot–Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis," Biometrics, The International Biometric Society, vol. 56(2), pages 455-463, June.
    9. repec:ags:stataj:122697 is not listed on IDEAS
    10. D. Y. Lin & D. Zeng, 2010. "On the relative efficiency of using summary statistics versus individual-level data in meta-analysis," Biometrika, Biometrika Trust, vol. 97(2), pages 321-332.
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