Article type
Year
Abstract
Background: The joint meta-analysis of two or more correlated outcomes, known as multivariate meta-analysis, is expected to improve precision compared to a series of independent meta-analyses and can, under circumstances, address selective outcome reporting bias. To date multivariate meta-analysis has been applied only to compare pairs of interventions. Further, its usefulness has been limited by the fact that within study-correlations are rarely reported in studies.
Objectives: To develop a model to synthesize multiple dichotomous outcomes over a network of studies that compare competing interventions and to introduce a simple procedure to elicit expert opinion for the within-study correlation.
Methods: We propose a flexible model for the meta-analysis of multiple outcomes and multiple treatments. By making plausible assumptions about the heterogeneity, we simplify the model and decrease the number of between-studies correlation parameters that need to be estimated. When within-study correlations are not available, we show how these can be estimated from a set of appropriate probability parameters for which expert opinion can be easily obtained. We fit the model within a Bayesian framework that offers maximum flexibility and allows incorporation of expert opinion.
Results: We apply our model to a network of 13 treatments for acute mania and we consider 2 correlated outcomes: efficacy and acceptability. Our multiple-outcomes network meta-analysis model produces considerably narrower confidence intervals compared to the simple network meta-analysis. The gain in precision is significant for the efficacy outcome because many studies report only on acceptability and our model allows for borrowing strength across outcomes.
Conclusions: The suggested model constitutes a viable candidate for performing network meta-analysis for multiple outcomes within a Bayesian framework and improves precision in the estimates and ranking. The suggested prior elicitation method is straightforward and easily understood by clinicians.
Objectives: To develop a model to synthesize multiple dichotomous outcomes over a network of studies that compare competing interventions and to introduce a simple procedure to elicit expert opinion for the within-study correlation.
Methods: We propose a flexible model for the meta-analysis of multiple outcomes and multiple treatments. By making plausible assumptions about the heterogeneity, we simplify the model and decrease the number of between-studies correlation parameters that need to be estimated. When within-study correlations are not available, we show how these can be estimated from a set of appropriate probability parameters for which expert opinion can be easily obtained. We fit the model within a Bayesian framework that offers maximum flexibility and allows incorporation of expert opinion.
Results: We apply our model to a network of 13 treatments for acute mania and we consider 2 correlated outcomes: efficacy and acceptability. Our multiple-outcomes network meta-analysis model produces considerably narrower confidence intervals compared to the simple network meta-analysis. The gain in precision is significant for the efficacy outcome because many studies report only on acceptability and our model allows for borrowing strength across outcomes.
Conclusions: The suggested model constitutes a viable candidate for performing network meta-analysis for multiple outcomes within a Bayesian framework and improves precision in the estimates and ranking. The suggested prior elicitation method is straightforward and easily understood by clinicians.