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Activity Number: 555
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304045
Title: Multivariate Meta-Analysis Box-Cox Transformation Models for Individual Patient Data with Applications to Evaluation of Cholesterol-Lowering Drugs
Author(s): Ming-Hui Chen*+ and Sungduk Kim and Joseph Ibrahim and Arvind K Shah and Jianxin Lin
Companies: University of Connecticut and National Institutes of Health and The University of North Carolina at Chapel Hill and Merck Research Laboratories and Merck Research Laboratories
Address: Department of Statistics, Storrs, CT, 06269-4120, United States
Keywords: DIC ; Heterogeneity ; IPD ; Markov chain Monte Carlo ; Multiple trials ; Random effects

We propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. The Deviance Information Criterion (DIC) is used to select the best transformation model. An efficient MCMC sampling algorithm is developed to carry out posterior computations. The proposed model is motivated from a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional responses consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG). In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate as these variables are correlated. A detailed analysis of these data is carried out using the proposed methodology.

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