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Title: Multivariate meta-analysis of mixed outcomes: a Bayesian approach
Authors: Bujkiewicz, Sylwia
Thompson, John R.
Sutton, Alex J.
Cooper, Nicola J.
Harrison, M. J.
Symmons, D. P.
Abrams, Keith R.
First Published: 30-Apr-2013
Publisher: John Wiley & Sons, Ltd.
Citation: Statistics in Medicine, 2013, 32 (22), pp. 3926-3943
Abstract: Multivariate random effects meta-analysis (MRMA) is an appropriate way for synthesizing data from studies reporting multiple correlated outcomes. In a Bayesian framework, it has great potential for integrating evidence from a variety of sources. In this paper, we propose a Bayesian model for MRMA of mixed outcomes, which extends previously developed bivariate models to the trivariate case and also allows for combination of multiple outcomes that are both continuous and binary. We have constructed informative prior distributions for the correlations by using external evidence. Prior distributions for the within-study correlations were constructed by employing external individual patent data and using a double bootstrap method to obtain the correlations between mixed outcomes. The between-study model of MRMA was parameterized in the form of a product of a series of univariate conditional normal distributions. This allowed us to place explicit prior distributions on the between-study correlations, which were constructed using external summary data. Traditionally, independent 'vague' prior distributions are placed on all parameters of the model. In contrast to this approach, we constructed prior distributions for the between-study model parameters in a way that takes into account the inter-relationship between them. This is a flexible method that can be extended to incorporate mixed outcomes other than continuous and binary and beyond the trivariate case. We have applied this model to a motivating example in rheumatoid arthritis with the aim of incorporating all available evidence in the synthesis and potentially reducing uncertainty around the estimate of interest.
DOI Link: 10.1002/sim.5831
eISSN: 1097-0258
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2013 The Authors. Statistics in Medicine Published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Appears in Collections:Published Articles, Dept. of Health Sciences

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