title: Predictive distributions were developed for the extent of heterogeneity in meta-analyses of continuous outcome data.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 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.
authors.forename: Kirsty M
authors.initials: KM
authors.lastname: Rhodes
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR, UK.
authors.forename: Rebecca M
authors.initials: RM
authors.lastname: Turner
authors.affiliation: 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.
authors.forename: Julian P T
authors.initials: JP
authors.lastname: Higgins
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008390:Markov Chains; D015201:Meta-Analysis as Topic; D009774:Obstetrics; D011247:Pregnancy; D012107:Research Design
keywords: Bayesian analysis; Continuous data; Heterogeneity; Intervention studies; Meta-analysis; Standardized mean difference