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J Clin Epidemiol. 2015 Jan;68(1):52-60. doi: 10.1016/j.jclinepi.2014.08.012. Epub 2014 Oct 7.

Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data.

Author information

1
MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK. Electronic address: kirsty.rhodes@mrc-bsu.cam.ac.uk.
2
MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
3
School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK; Centre for Reviews and Dissemination, A/B Block, Alcuin College, University of York, York, YO10 5DD, UK.

Abstract

OBJECTIVES:

Estimation of between-study heterogeneity is problematic in small meta-analyses. Bayesian meta-analysis is beneficial because it allows incorporation of external evidence on heterogeneity. To facilitate this, we provide empirical evidence on the likely heterogeneity between studies in meta-analyses relating to specific research settings.

STUDY DESIGN AND SETTING:

Our analyses included 6,492 continuous-outcome meta-analyses within the Cochrane Database of Systematic Reviews. We investigated the influence of meta-analysis settings on heterogeneity by modeling study data from all meta-analyses on the standardized mean difference scale. Meta-analysis setting was described according to outcome type, intervention comparison type, and medical area. Predictive distributions for between-study variance expected in future meta-analyses were obtained, which can be used directly as informative priors.

RESULTS:

Among outcome types, heterogeneity was found to be lowest in meta-analyses of obstetric outcomes. Among intervention comparison types, heterogeneity was lowest in meta-analyses comparing two pharmacologic interventions. Predictive distributions are reported for different settings. In two example meta-analyses, incorporating external evidence led to a more precise heterogeneity estimate.

CONCLUSION:

Heterogeneity was influenced by meta-analysis characteristics. Informative priors for between-study variance were derived for each specific setting. Our analyses thus assist the incorporation of realistic prior information into meta-analyses including few studies.

KEYWORDS:

Bayesian analysis; Continuous data; Heterogeneity; Intervention studies; Meta-analysis; Standardized mean difference

PMID:
25304503
PMCID:
PMC4270451
DOI:
10.1016/j.jclinepi.2014.08.012
[Indexed for MEDLINE]
Free PMC Article

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