Meta-analysis in clinical trials revisited

Contemporary Clinical Trials

Volumn 45, Issue , 2015, Pages 139-145

  DerSimonian, Rebecca ^a   Laird, Nan ^b  

^a NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Big Data; Clinical trials; Genome Wide Association Studies; Heterogeneity; Meta analysis; Random effects model

Indexed keywords

ARTICLE; CITATION ANALYSIS; CLINICAL RESEARCH; DATA ANALYSIS; GENETIC VARIABILITY; MEDICAL RESEARCH; CLINICAL TRIAL (TOPIC); GENOME-WIDE ASSOCIATION STUDY; HUMAN; META ANALYSIS (TOPIC); METHODOLOGY; PROCEDURES; STATISTICAL MODEL;

CLINICAL TRIALS AS TOPIC; GENOME-WIDE ASSOCIATION STUDY; HUMANS; META-ANALYSIS AS TOPIC; MODELS, STATISTICAL; RESEARCH DESIGN;

EID: 84955395509     PISSN: 15517144     EISSN: 15592030     Source Type: Journal    
DOI: 10.1016/j.cct.2015.09.002     Document Type: Article

References (32)

1. DerSimonian R., Laird N.M. Meta-analysis in clinical trials. Control. Clin. Trials 1986, 7:177-188.
2. Glass G.V. Primary, secondary, and meta-analysis of research. Educ. Res. 1976, 5:3-8.
3. Rosenthal R., Rubin D.B. Interpersonal expectancy effects: the first 345 studies. Behav. Brain Sci. 1978, 1:377-386.
4. Mosteller F. The Tennessee study of class size in the early school grades. Futur. Child. 1995, 5(2):113-127.
5. Mosteller F., Light R.J., Sachs J.A. Sustaining inquiry in education: lessons from skill grouping and class size. Harv. Educ. Rev. 1996, 66(4):797-842.
6. Slack W., Porter D. The scholastic aptitude test: a critical appraisal. Harv. Educ. Rev. 1980, 66:1-27.
7. DerSimonian R., Laird N.M. Evaluating the effect of coaching on SAT scores: a meta-analysis. Harv. Educ. Rev. 1983, 53:1-15.
8. Board T.C. Research notes: coaching and the SAT® I. Office of Research and Development RN-06 1999.
9. Cochran W.G. The combination of estimates from different experiments. Biometrics 1954, 10:101-129.
10. DerSimonian R., Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemp. Clin. Trials 2007, 28:105-114.
11. Jackson D., Boden J., Baker R. How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient by harder to compute counterparts?. J. Stat. Plan. Infer. 2010, 140:961-970.
12. Kontopantelis E., Reeves D. Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: a simulation study. Stat. Methods Med. Res. 2012, 21(4):409-426.
13. Kontopantelis E., Reeves D. Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: a comparison between DerSimonian-Laird and restricted maximum likelihood. Stat. Methods Med. Res. 2012, 21(6):657-659.
14. Jackson D., White I.R., Thompson S.G. Extending DerSimonian and Laird's methodology to perform multivariate random-effects meta-analyses. Stat. Med. 2010, 29:1282-1297.
15. Jackson D., White I.R., Riley R.D. A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biom. J. 2013, 55(2):231-245.
16. Chen H., Manning A.K., Dupuis J. A method of moments estimator for random effect multivariate meta-analysis. Biometrics 2012, 68:1278-1284.
17. Biggerstaff B.J., Jackson D. The exact distribution of Cochran's heterogeneity statistic in one-way random effects meta-analysis. Stat. Med. 2008, 27:6093-6110.
18. Kulinskaya E., Dollinger M.B., Bjorkestol K. On the moments of Cochran's Q statistic under the null hypothesis, with application to the meta-analysis of risk difference. Res. Synth. Methods 2011, 2:254-270.
19. Kulinskaya E., Dollinger M.B., Bjorkestol K. Testing for homogeneity in meta-analysis I. The one-parameter case: standardized mean difference. Biometrics 2011, 67:203-212.
20. Jackson D., Turner R., Rhodes K., Viechtbauer W. Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models. BMC Med. Res. Methodol. 2014, 14:103.
21. Jackson D. Confidence intervals for the between-study variance in random effects meta-analysis using generalized Cochran heterogeneity statistics. Res. Synth. Methods. 2013, 4:220-229.
22. Higgins J.P.T., Thompson S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 2002, 21:1539-1558.
23. Berkey C.S., Hoaglin D.C., Mosteller F., Colditz G.A. A random-effects regression model for meta-analysis. Stat. Med. 1995, 14:395-411.
24. Thompson S.G., Higgins J.P.T. How should meta-regression analyses be undertaken and interpreted?. Stat. Med. 2002, 21:1559-1573.
25. IntHout N.M., Ioannidis J.P.A., Borm G.F. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med. Res. Methodol. 2014, 14:25.
26. Han B., Eskin E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 2011, 88:586-598.
27. Sidik K., Jonkman J.N. A note on variance estimation on random effects meta-regression. J. Biopharm. Stat. 2005, 15:823-838.
28. Laird N.M., Mosteller F. Some statistical methods for combining experimental results. Int. J. Technol. Assess. Health Care 1990, 6:5-30.
29. Hartung J., Knapp G. A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat. Med. 2001, 20:3875-3889.
30. Lin D., Zeng D. Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data. Genet. Epidemiol. 2010, 34:60-66.
31. Heid I.M., Huth C., Loos R.J.F., Kronenberg F., Adamkova V., et al. Meta-analysis of the INSIG2 association with obesity including 74,345 individuals: does heterogeneity of estimates relate to study design?. PLoS Genet. 2009, 5(10). 10.1371/journal.pgen.1000694.
32. Herbert A., Gerry N.P., McQueen M.B., Heid I.M., Pfeufer A., et al. A common genetic variant is associated with adult and childhood obesity. Science 2006, 312:279-283.