Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews

International Journal of Epidemiology

Volumn 41, Issue 3, 2012, Pages 818-827

  Turner, Rebecca M ^a   Davey, Jonathan ^a   Clarke, Mike J ^b   Thompson, Simon G ^c   Higgins, Julian PT ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Bayesian analysis; Heterogeneity; Intervention studies; Meta analysis

Indexed keywords

BAYESIAN ANALYSIS; CONFIDENCE INTERVAL; DATA INTERPRETATION; DATABASE; EMPIRICAL ANALYSIS; HEALTH CARE; HETEROGENEITY; META-ANALYSIS; MORTALITY; PREDICTION;

ARTICLE; BAYES THEOREM; COCHRANE LIBRARY; HUMAN; INFORMATION PROCESSING; INTERVENTION STUDY; MEDICAL RESEARCH; METHODOLOGY; OUTCOME ASSESSMENT; PREDICTIVE VALIDITY; PRIORITY JOURNAL; SAMPLE SIZE;

BAYES THEOREM; HUMANS; META-ANALYSIS AS TOPIC; OUTCOME ASSESSMENT (HEALTH CARE); RESEARCH DESIGN;

EID: 84864508284     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dys041     Document Type: Article

References (23)

1. DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled Clin Trials 1986;7:177-88.
2. Higgins JPT, Thompson SG, Spiegelhalter DJ. A reevaluation of random-effects meta-analysis. J Royal Stat Soc A 2009;172:137-59.
3. Jackson D, Bowden J, Baker R. How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts? J Stat Plan Infer 2010; 140:961-70.
4. Smith TC, Spiegelhalter DJ, Thomas A. Bayesian approaches to random-effects meta-analysis: a comparative study. Stat Med 1995;14:2685-99.
5. Sutton AJ, Abrams KR. Bayesian methods in metaanalysis and evidence synthesis. Stat Methods Med Res 2001;10:277-303.
6. Higgins JPT, Spiegelhalter DJ. Being sceptical about meta-analyses: a Bayesian perspective on magnesium trials in myocardial infarction. Int J Epidemiol 2002;31: 96-104.
7. Ades AE, Lu G, Higgins JPT. The interpretation of random-effects meta-analysis in decision models. Med Decis Making 2005;25:646-54.
8. Higgins JPT, Whitehead A. Borrowing strength from external trials in a meta-analysis. Stat Med 1996;15: 2733-49.
9. Davey J, Turner RM, Clarke MJ, Higgins JPT. Characteristics of meta-analyses and their component studies in the Cochrane Database of Systematic Reviews: a cross-sectional, descriptive analysis. BMC Med Res Methodol 2011;11:160.
10. Wood L. The Epidemiology of Bias in Randomized (clinical) Controlled Trials: a Meta-epidemiological Study. PhD thesis. University of Bristol, 2006.
11. Foundation for Health Services Research. Health Outcomes Research: A Primer. http://www.academyhealth.org/files/ publications/healthoutcomes.pdf (1 March 2011, date last accessed).
12. UK Clinical Research Collaboration. Health Research Classification System. http://www.hrcsonline.net/pages/hrcs _download (1 March 2011, date last accessed).
13. National Institute for Health and Clinical Excellence. NICE Taxonomy (subject encoding scheme). http://www.nice.org.uk/aboutnice/nicewebsitedevelopment/standards/ metadata/niceencodingschemes/nice_encoding_schemes. jsp (1 August 2009, date last accessed).
14. Lunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS- a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 2000;10:325-37.
15. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit (with discussion). J Royal Stat Soc B 2002;64:583-640.
16. Stanworth S, Massey E, Hyde C et al. Granulocyte transfusions for treating infections in patients with neutropenia or neutrophil dysfunction. Cochrane Database Syst Rev 2010, Issue 8; Art. No.: CD005339.
17. Lambert PC, Sutton AJ, Burton PR, Abrams KR, Jones DR. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med 2005;24:2401-28.
18. Hughes JR, Stead LF, Lancaster T. Antidepressants for smoking cessation. Cochrane Database Syst Rev 2011, Issue 8; Art. No.: CD000031.
19. Viechtbauer W. Confidence intervals for the amount of heterogeneity in meta-analysis. Stat Med 2007;26: 37-52.
20. Pullenayegum E. An informed reference prior for between-study heterogeneity in meta-analyses of binary outcomes. Stat Med 2011;30:3082-94.
21. Higgins JPT, Altman DG. Assessing risk of bias in included studies. In: Higgins JPT, Green S (eds). Cochrane Handbook for Systematic Reviews of Interventions. Chichester: John Wiley & Sons, 2008, pp. 187-241.
22. Welton NJ, Ades AE, Carlin JB, Altman DG, Sterne JAC. Models for potentially biased evidence in meta-analysis using empirically based priors. J Royal Stat Soc A 2009; 172:119-36.
23. Turner RM, Spiegelhalter DJ, Smith GCS, Thompson SG. Bias modelling in evidence synthesis. J Royal Stat Soc A 2009;172:21-47.