Abstract
BackgroundNetwork 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.MethodsThe 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.ResultsUnivariate 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.ConclusionsAccounting 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.
Highlights
Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation
In a decision making context where the synthesis is meant to inform a health economic evaluation, accounting for the correlations between effect estimates on different outcomes is important as the dependence between outcomes may have implication for estimating quality of life or economic consequences associated with treatment [13]
Model 2: Multivariate NMA We extend the univariate NMA model defined above to the multiple outcomes settings in order to account for correlations between intervention effects on different outcomes
Summary
Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. One area of meta-analysis that has seen significant methodological development is the application of multivariate statistical methods for the comparison of treatments on two or more endpoints (usually known as multivariate metaanalysis) [3,4,5,6,7,8] These methods are appealing because many studies and systematic reviews focus on broad health effects and typically report several outcome measures [4,6,9]. In such instances, the multivariate approach offers some advantages over separate univariate analyses including the ability to account for the inter-relationship between outcomes and borrow strength across studies as well as across outcomes [10] through modelling the correlation structure [7,11]. Ignoring information about the inter-relationships between beneficial and ‘side effect’ endpoints in such instances may have implications for quantifying the benefits associated with treatment
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