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Activity Number: 542 - New Research Synthesis Methods in Data Science
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract #300003 Presentation
Title: Bayesian Inference for Network Meta-Regression Using Multivariate Random Effects with Applications to Cholesterol-Lowering Drugs
Author(s): Joseph G Ibrahim* and Sungduk Kim and Ming-Hui Chen and Arvind Shah and Jianxin Lin and Hao Li and Andrew Tershakovec
Companies: UNC and NIH and University of Connecticut and Merck, Inc. and Merck, Inc. and Boehringer Ingelheim and Merck, Inc
Keywords: Meta-regression; Mulivariate Random Effects; Bayesian methods; Individual patient data; Bayesian Model Comparison

Many clinical trials have been carried out on safety and efficacy evaluation of cholesterol lowering drugs. To synthesize the results from different clinical trials, we examine treatment level (aggregate) network meta-data from 29 double-blind, randomized, active or placebo-controlled statins +/- Ezetimibe clinical trials on adult treatment-naïve patients with primary hypercholesterolemia. In this paper, we propose a new approach to carry out Bayesian inference for arm-based network meta-regression. Specifically, we group the variances of the random effects based on the clinical nature of treatments, and the determination of the number of groups and group membership is further guided by Bayesian model comparison criteria. The proposed approach is especially useful when some treatment arms are involved in only a single trial. In addition, a new Metropolis-within-Gibbs sampling algorithm is developed to carry out the posterior computations. In particular, the correlation matrix is generated from its full conditional distribution via partial correlations. The proposed methodology is further applied to analyze the network meta-data from 29 trials with 11 treatment arms.

Authors who are presenting talks have a * after their name.

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