Joint synthesis of multiple correlated outcomes in networks of interventions.

Journal article

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.

Authors: Efthimiou, O; Mavridis, D; Riley, R; Cipriani, A; Salanti, G

• Journal: Biostatistics (Oxford, England)
• Volume: 16
• Issue: 1
• Pages: 84-97
• Publication date: 2015-01-01
• DOI: 10.1093/biostatistics/kxu030
• EISSN: 1468-4357
• ISSN: 1465-4644
• Language: English
• Keywords: Heterogeneity; Multivariate meta-analysis; Correlation; Mixed-treatment comparison