A note on a Bayesian procedure for meta-analysis of rare data
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.2239Keywords:
Análisis bayesiano, Farlie-Gumbel-Morgenstern link, meta-análisis, Bayesian analysis, meta-analysisAbstract
The propose of this paper is to develop a Bayesian procedure that adequately account for studies with zero observations in meta-analysis and then we focus the problem in the context of the Bayesian selection models. Also, attention is focused to the link distribution between effectiveness in each study/center and the meta-effectiveness.
We present an objective Bayesian method where all quantities of interest jointly with a Bayesian test for equality between treatments are also obtained. A couple of examples with is developed in depth using the proposed Bayesian meta-analysis for the binomial model. Basically, we obtain a Bayesian model for meta-analysis for sparse binomial data without considering transformations and/or corrections in variable/parameters. In respect to the examples considered, we do not find a relevant difference between treatments.
Downloads
References
Bradburn, M.J.; Deeks, J.J.; Berlin, J.A. y Russell-Localio, A. (2007). Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events. Statistics in Medicine, 26: 53-77.
Jeffreys, H. (1961). Theory of probability. Oxford: Oxford University Press.
Hemminki, E. y McPherson, K. (1997). Impact of postmenopausal hormone therapy on cardiovascular events and cancer: pooled data from clinical trials. British Medical Journal; 315: 149-153.
Lambert, P.C.; Sutton, A.J.; Burton, P.R.; Abrams, K.R. y Jones, D.R. (2005). How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Statistics in Medicine; 24: 2401-2428.
Moreno, E.; Vázquez-Polo, F.J. y Negrín, M.A. (2014 ). Objective Bayesian meta-analysis for sparse discrete data. Statistics in Medicine; 33: 3676-3692.
Morgenstern, D. (1956). Einfache Beispiele zweidimensionaler Verteilungen. Mitteilungsblatt für Mathematische Statistik, 8: 234-235.
Sedrakyan, A.; Wu, A.W.; Parashar, A.; Bass, E.B. y Treasure, T. (2006). Off-Pump surgery is associated with reduced occurrence of stroke and other morbidity as compared with traditional coronary artery bypass grafting. A meta-analysis of systematically reviewed trials. Stroke, 37: 2759-2769.
Sutton, A.J.; Abrams, K.R. y Jones, D.R. (2001). An illustrated guide to the methods in meta-analysis. Journal of Evaluation in Clinical Practice, 7: 135-148.
Sutton, A.J. y Abrams, K.R. (2001). Bayesian methods in meta-analysis and evidence synthesis. Statistical Methods in Medical Research, 10: 277-303.
Sutton, A.J.; Cooper, N.J.; Lambert, P.C.; Jones, D.R.; Abrams, K.R. y Sweeting, M.J. (2002). Meta-analysis of rare and adverse event data. Expert Rev. Pharmacoeconomics Outcomes Res., 2: 367-369.
Sweeting, M.J.; Sutton, A.J. y Lambert, P.C. (2004). What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Statistics in Medicine; 243: 1351-1375.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2015 Revista de Métodos Cuantitativos para la Economía y la Empresa
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.