Exploiting multivariate network meta-analysis: A calibrated Bayesian composite likelihood inference.

ABSTRACT: Multivariate network meta-analysis has emerged as a powerful tool in evidence synthesis by incorporating multiple outcomes and treatments. Despite its advantages, this method comes with methodological challenges, such as the issue of unreported within-study correlations among treatments and outcomes, which potentially lead to misleading conclusions. In this paper, we proposed a calibrated Bayesian composite likelihood approach to overcome this limitation. The proposed method eliminated the need to specify a full likelihood function while allowing for the unavailability of within-study correlations among treatments and outcomes. Additionally, we developed a hybrid Gibbs sampler algorithm along with the Open-Faced Sandwich post-sampling adjustment to enable robust posterior inference. Throug

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11230323 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature