A0215
Title: A robust and computational-efficient method for multiple-outcome network meta-analysis
Authors: Yong Chen - Univ. of Pennsylvania (United States) [presenting]
Abstract: In many biomedical settings, there is an increasing number of interventions available for a disease condition. It is critical for clinical decision-making to accurately evaluate and compare the relative efficacy and safety, as well as other patient centered outcomes of these interventions. We propose a network meta-analysis model for multiple clinical outcomes. Inspired by the idea of composite likelihood, the proposed method only requires the specification of the marginal distribution of each outcome, and a pseudolikelihood is then constructed under a working independence assumption. We also develop a novel inferential procedure with an associated efficient computational algorithm, which is statistically robust (i.e., requires minimal distributional assumptions) and computational stable and fast. We will illustrate our method through multiple case studies including a network meta-analysis of comparing 12 labor induction methods. The proposed composite likelihood-based multivariate network meta-analysis method leads to a computationally efficient algorithm with robust statistical inference, while being able to take multiple outcomes into consideration.
