Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/3378
Title: Bivariate random-effects meta-analysis and the estimation of between-study correlation
Authors: Riley, Richard D.
Abrams, Keith R.
Sutton, Alex J.
Lambert, Paul C.
Thompson, John R.
First Published: 12-Jan-2007
Publisher: BioMed Central Ltd
Citation: BMC Medical Research Methodology, 2007, 7 : 3
Abstract: Background: When multiple endpoints are of interest in evidence synthesis, a multivariate meta-analysis can jointly synthesise those endpoints and utilise their correlation. A multivariate random-effects meta-analysis must incorporate and estimate the between-study correlation (ρ[subscript B]). Methods: In this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate random-effects meta-analysis (BRMA). We consider two applied examples, one involving a diagnostic marker and the other a surrogate outcome. These motivate a simulation study where estimation properties from BRMA are compared with those from two separate univariate random-effects meta-analyses (URMAs), the traditional approach. Results: The normal BRMA model estimates ρ[subscript B] as -1 in both applied examples. Analytically we show this is due to the maximum likelihood estimator sensibly truncating the between-study covariance matrix on the boundary of its parameter space. Our simulations reveal this commonly occurs when the number of studies is small or the within-study variation is relatively large; it also causes upwardly biased between-study variance estimates, which are inflated to compensate for the restriction on ρ^[subscript B]. Importantly, this does not induce any systematic bias in the pooled estimates and produces conservative standard errors and mean-square errors. Furthermore, the normal BRMA is preferable to two normal URMAs; the mean-square error and standard error of pooled estimates is generally smaller in the BRMA, especially given data missing at random. For meta-analysis of proportions we then show that a generalised BRMA model is better still. This correctly uses a binomial rather than normal distribution, and produces better estimates than the normal BRMA and also two generalised URMAs; however the model may sometimes not converge due to difficulties estimating ρ[subscript B]. Conclusion: A BRMA model offers numerous advantages over separate univariate synthesises; this paper highlights some of these benefits in both a normal and generalised modelling framework, and examines the estimation of between-study correlation to aid practitioners.
DOI Link: 10.1186/1471-2288-7-3
eISSN: 1471-2288
Links: http://hdl.handle.net/2381/3378
http://www.biomedcentral.com/1471-2288/7/3
Version: Publisher Version
Status: Peer-reviewed
Type: Article
Rights: Copyright © 2007 Riley et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted 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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