Joint synthesis of multiple correlated outcomes in networks of interventions

Biostatistics. 2015 Jan;16(1):84-97. doi: 10.1093/biostatistics/kxu030. Epub 2014 Jul 2.


Multiple outcomes multivariate meta-analysis (MOMA) is gaining in popularity as a tool for jointly synthesizing evidence coming from studies that report effect estimates for multiple correlated outcomes. Models for MOMA are available for the case of the pairwise meta-analysis of two treatments for multiple outcomes. Network meta-analysis (NMA) can be used for handling studies that compare more than two treatments; however, there is currently little guidance on how to perform an MOMA for the case of a network of interventions with multiple outcomes. The aim of this paper is to address this issue by proposing two models for synthesizing evidence from multi-arm studies reporting on multiple correlated outcomes for networks of competing treatments. Our models can handle continuous, binary, time-to-event or mixed outcomes, with or without availability of within-study correlations. They are set in a Bayesian framework to allow flexibility in fitting and assigning prior distributions to the parameters of interest while fully accounting for parameter uncertainty. As an illustrative example, we use a network of interventions for acute mania, which contains multi-arm studies reporting on two correlated binary outcomes: response rate and dropout rate. Both multiple-outcomes NMA models produce narrower confidence intervals compared with independent, univariate network meta-analyses for each outcome and have an impact on the relative ranking of the treatments.

Keywords: Correlation; Heterogeneity; Mixed-treatment comparison; Multivariate meta-analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bipolar Disorder / drug therapy
  • Data Interpretation, Statistical*
  • Humans
  • Meta-Analysis as Topic*
  • Models, Statistical*
  • Multivariate Analysis*
  • Outcome Assessment, Health Care / methods*