Efficient Network Meta-Analysis: A Confidence Distribution Approach
*Dungang Liu, University of Cincinnati
Keywords: Mixed treatment comparisons, multiple comparisons, random effects models
Current network meta-analysis approaches are derived from either conventional pairwise meta-analysis or hierarchical Bayesian methods. This paper introduces a new approach for network meta-analysis by combining confidence distributions (CDs). The proposed CD approach can efficiently integrate all studies in the network and provide inference for all treatments, even when individual studies contain only comparisons of subsets of the treatments. Through numerical studies with real and simulated data sets, the proposed approach is shown to outperform---or at least equal---the traditional pairwise meta-analysis and a commonly used Bayesian hierarchical model. Although the Bayesian approach may yield comparable results with a suitably chosen prior, it is highly sensitive to the choice of priors (especially for the between-trial covariance structure), which is often subjective. The CD approach is a general frequentist approach and prior-free. Moreover, it can always provide a proper inference for all the treatment effects, regardless of the between-trial covariance structure.