A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons.

ABSTRACT: Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two treatments), and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast-base

SUBMITTER: Hong H 

PROVIDER: S-EPMC4779385 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature