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A comparison of three different models for estimating relative risk in meta-analysis of clinical trials under unobserved heterogeneity

Kuhnert, R. and Böhning, D. A. W. (2007) A comparison of three different models for estimating relative risk in meta-analysis of clinical trials under unobserved heterogeneity. Statistics in Medicine, 26 (11). pp. 2277-2296. ISSN 0277-6715

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To link to this article DOI: 10.1002/sim.2710

Abstract/Summary

We focus on the comparison of three statistical models used to estimate the treatment effect in metaanalysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model. Copyright (C) 2006 John Wiley & Sons, Ltd.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:10034
Uncontrolled Keywords:MAIPD, multi-level model, profile likelihood, approximate likelihood, mixture model, unobserved heterogeneity, GENERALIZED LINEAR-MODELS, COMPONENTS

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