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

Kirsty M Rhodes, Rebecca M Turner, Julian P T Higgins

Research output: Contribution to journalArticle (Academic Journal)

116 Citations (Scopus)

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.

Original languageEnglish
JournalJournal of Clinical Epidemiology
DOIs
Publication statusPublished - 7 Oct 2014

Bibliographical note

Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Structured keywords

  • ConDuCT-II

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