Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

BMC Medical Research Methodology

Alex J Sutton²  David R Jones²  Denise Kendrick¹  Stephanie J Hubbard²  Sylwia Bujkiewicz²  Nicola J Cooper²  Felix A Achana² 

[1] Division of Primary Care Community Health Sciences, Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham NG7 2RD, UK;Biostatistics Group, Department of Health Sciences, University of Leicester, University Road, Leicester LE1 7RH, UK

关键词: WinBUGS;    Multivariate;    Multiple outcomes;    Mixed treatment comparisons;    Network meta-analysis;   

Others  :  1091399
DOI  :  10.1186/1471-2288-14-92

 received in 2014-01-08, accepted in 2014-07-02,  发布年份 2014

【 摘 要 】

Background

Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes.

Methods

The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations.

Results

Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis.

Conclusions

Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately.

【 授权许可】

   
2014 Achana et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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