Bayesian Network Meta-Regression Hierarchical Models Using Heavy-Tailed Multivariate Random Effects with Covariate-Dependent Variances

• Activity Number: 418 - Recent Developments in Network Meta-Analysis
• Type: Invited
• Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
• Sponsor: ENAR
• Author(s): Hao Li and Daeyoung Lim and Ming-Hui Chen* and Joseph G Ibrahim and Sungduk Kim and Arvind Shah and Jianxin Lin
• Companies: University of Connecticut and University of Connecticut and UCONN and UNC and National Cancer Institute and Merck, Inc. and Merck, Inc.
• Keywords: Arm-based model; Collapsed Gibbs sampling; Multivariate t distribution; Surface under the cumulative ranking curve (SUCRA); Triglycerides (TG)

Abstract

Network meta-regression (NMR) allows us to incorporate potentially important covariates into network meta-analysis. In this paper, we propose a Bayesian network meta-regression hierarchical model and assume a general multivariate t distribution for the random treatment effects. The multivariate t distribution is desired for heavy-tailed random effects and converges to the multivariate normal distribution when the degrees of freedom go to infinity. Moreover, in NMA data, some treatments are compared only in a single study. To overcome such sparsity, we propose a log-linear regression model for the variances of the random effects and incorporate aggregate covariates into modeling the variance components. We develop a Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via the collapsed Gibbs technique. We further use the Deviance Information Criterion (DIC) and the logarithm of the Pseudo-marginal likelihood (LPML) for model comparison. A simulation study is conducted and a detailed analysis from our motivating case study is carried out to further demonstrate the proposed methodology.