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An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios
dc.creator | Efthimiou, O. | en |
dc.creator | Mavridis, D. | en |
dc.creator | Cipriani, A. | en |
dc.creator | Leucht, S. | en |
dc.creator | Bagos, P. | en |
dc.creator | Salanti, G. | en |
dc.date.accessioned | 2015-11-23T10:26:05Z | |
dc.date.available | 2015-11-23T10:26:05Z | |
dc.date.issued | 2014 | |
dc.identifier | 10.1002/sim.6117 | |
dc.identifier.issn | 0277-6715 | |
dc.identifier.uri | http://hdl.handle.net/11615/27292 | |
dc.description.abstract | A multivariate meta-analysis of two or more correlated outcomes is expected to improve precision compared with a series of independent, univariate meta-analyses especially when there are studies reporting some but not all outcomes. Multivariate meta-analysis requires estimates of the within-study correlations, which are seldom available. Existing methods for analysing multiple outcomes simultaneously are limited to pairwise treatment comparisons. We propose a model for a joint, simultaneous synthesis of multiple dichotomous outcomes in a network of interventions and introduce a simple way to elicit expert opinion for the within-study correlations by utilizing a set of conditional probability parameters. We implement our multiple-outcomes network meta-analysis model within a Bayesian framework, which allows incorporation of expert information. As an example, we analyse two correlated dichotomous outcomes, response to the treatment and dropout rate, in a network of pharmacological interventions for acute mania. The produced estimates have narrower confidence intervals compared with the simple network meta-analysis. We conclude that the proposed model and the suggested prior elicitation method for correlations constitute a useful framework for performing network meta-analysis for multiple outcomes. Copyright (c) 2014 John Wiley & Sons, Ltd. | en |
dc.source | Statistics in Medicine | en |
dc.source.uri | <Go to ISI>://WOS:000335772800009 | |
dc.subject | mixed treatment | en |
dc.subject | correlated outcomes | en |
dc.subject | within-study correlation | en |
dc.subject | between-study correlation | en |
dc.subject | Bayesian | en |
dc.subject | RANDOM-EFFECTS METAANALYSIS | en |
dc.subject | WITHIN-STUDY COVARIANCES | en |
dc.subject | MULTIVARIATE | en |
dc.subject | METAANALYSIS | en |
dc.subject | META-REGRESSION | en |
dc.subject | TRIALS | en |
dc.subject | IMPACT | en |
dc.subject | DISTRIBUTIONS | en |
dc.subject | FRAMEWORK | en |
dc.subject | VARIANCE | en |
dc.subject | EFFICACY | en |
dc.subject | Mathematical & Computational Biology | en |
dc.subject | Public, Environmental & | en |
dc.subject | Occupational Health | en |
dc.subject | Medical Informatics | en |
dc.subject | Medicine, Research & | en |
dc.subject | Experimental | en |
dc.subject | Statistics & Probability | en |
dc.title | An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios | en |
dc.type | journalArticle | en |
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