An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios
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 co...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
John Wiley and Sons Ltd
2014
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| _version_ | 1826302806713171968 |
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| author | Efthimiou, O Mavridis, D Cipriani, A Leucht, S Bagos, P Salanti, G |
| author_facet | Efthimiou, O Mavridis, D Cipriani, A Leucht, S Bagos, P Salanti, G |
| author_sort | Efthimiou, O |
| collection | OXFORD |
| description | 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. © 2014 John Wiley and Sons, Ltd. |
| first_indexed | 2024-03-07T05:53:01Z |
| format | Journal article |
| id | oxford-uuid:e98c9e99-a3c8-4f9d-b054-56ca8c1e9af9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:53:01Z |
| publishDate | 2014 |
| publisher | John Wiley and Sons Ltd |
| record_format | dspace |
| spelling | oxford-uuid:e98c9e99-a3c8-4f9d-b054-56ca8c1e9af92022-03-27T10:55:05ZAn approach for modelling multiple correlated outcomes in a network of interventions using odds ratiosJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e98c9e99-a3c8-4f9d-b054-56ca8c1e9af9EnglishSymplectic Elements at OxfordJohn Wiley and Sons Ltd2014Efthimiou, OMavridis, DCipriani, ALeucht, SBagos, PSalanti, GA 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. © 2014 John Wiley and Sons, Ltd. |
| spellingShingle | Efthimiou, O Mavridis, D Cipriani, A Leucht, S Bagos, P Salanti, G An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios |
| title | An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios |
| title_full | An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios |
| title_fullStr | An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios |
| title_full_unstemmed | An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios |
| title_short | An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios |
| title_sort | approach for modelling multiple correlated outcomes in a network of interventions using odds ratios |
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