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Activity Number: 254 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #306917
Title: New Development of Bayesian Inconsistency Detection for Network Meta-Analysis
Author(s): Cheng Zhang* and Ming-Hui Chen and Joseph G Ibrahim and Sungduk Kim and Jianxin Lin and Arvind Shah and Hao Li
Companies: University of Connecticut and University of Connecticut and UNC and NIH and Merck, Inc. and Merck, Inc. and Boehringer Ingelheim
Keywords: Network Meta-Analysis; Bayesian ; Inconsistency detection

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 clinical trials with statins or statins plus Ezetimibe on adult treatment-naive patients with primary hypercholesterolemia. In this paper, we construct general linear hypotheses to investigate consistency under a general fixed effects model without any assumptions. Some interesting results are established on equivalence between consistency assumptions on the treatment effect parameters and the hypotheses about certain contrasts of parameters. A general algorithm is developed to compute the contrast matrix under consistency assumptions. Furthermore, a new Bayesian approach is developed to detect inconsistency. Simulation studies are carried out. We apply the proposed methodology to conduct an analysis of 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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