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• W2156696492 abstract "Research Synthesis MethodsVolume 6, Issue 3 p. 287-289 Commentary We know less than we should about methods of meta-analysis David C. Hoaglin, Corresponding Author David C. Hoaglin Independent consultant, Sudbury, MA, USA Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA Correspondence to: David C. Hoaglin, 73 Hickory Road, Sudbury, MA 01776, USA. E-mail: [email protected]Search for more papers by this author David C. Hoaglin, Corresponding Author David C. Hoaglin Independent consultant, Sudbury, MA, USA Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA Correspondence to: David C. Hoaglin, 73 Hickory Road, Sudbury, MA 01776, USA. E-mail: [email protected]Search for more papers by this author First published: 10 June 2015 https://doi.org/10.1002/jrsm.1146Citations: 18Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. References Biggerstaff BJ, Jackson D. 2008. The exact distribution of Cochran's heterogeneity statistic in one-way random effects meta-analysis. Statistics in Medicine 27: 6093– 6110. Brockwell SE, Gordon IR. 2001. A comparison of statistical methods for meta-analysis. Statistics in Medicine 20: 825– 840. Cochran WG. 1954. The combination of estimates from different experiments. Biometrics 10: 101– 129. Cornell JE, Mulrow CD, Localio R, Stack CB, Meibohm AR, Guallar E, Goodman SN. 2014. Random-effects meta-analysis of inconsistent effects: a time for change. Annals of Internal Medicine 160: 267– 270. DerSimonian R, Laird N. 1986. Meta-analysis in clinical trials. Controlled Clinical Trials 7: 177– 188. Hamza TH, van Houwelingen HC, Stijnen T. 2008. The binomial distribution of meta-analysis was preferred to model within-study variability. Journal of Clinical Epidemiology 61: 41– 51. Hedges LV. 1982. Estimation of effect size from a series of independent experiments. Psychological Bulletin 92: 490– 499. Higgins JPT, Thompson SG. 2002. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21: 1539– 1558. Huber PJ. 1981. Robust Statistics. New York: Wiley. IntHout J, Ioannidis JPA, Borm GF. 2014. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Medical Research Methodology 14: 25. Jackson D, Bowden J, Baker R. 2010. How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts? Journal of Statistical Planning and Inference 140: 961– 970. Kulinskaya E, Dollinger MB, Bjørkestøl K. 2011a. Testing for homogeneity in meta-analysis I. The one-parameter case: standardized mean difference. Biometrics 67: 203– 212. Kulinskaya E, Dollinger MB, Bjørkestøl K. 2011b. On the moments of Cochran's Q statistic under the null hypothesis, with application to the meta-analysis of risk difference. Research Synthesis Methods 2: 254– 270. Sidik K, Jonkman JN. 2002. A simple confidence interval for meta-analysis. Statistics in Medicine 21: 3153– 3159. Citing Literature Volume6, Issue3Special Issue: The Origins of Modern Meta‐analysisSeptember 2015Pages 287-289 ReferencesRelatedInformation" @default.
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