PALM: Patient-centered Treatment Ranking via Large-scale Multivariate Network Meta-analysis

The growing number of available treatment options have led to urgent needs for reliable answers when choosing the best course of treatment for a patient. As it is often infeasible to compare a large number of treatments in a single randomized controlled trial, multivariate network meta-analyses (NMAs) are used to synthesize evidence from existing trials of a subset of the available treatments, where outcomes related to both efficacy and safety are considered simultaneously. However, these large-scale multiple-outcome NMAs have created challenges to existing methods due to the increasingly complexity of the unknown correlation structures between different outcomes and treatment comparisons. In this paper, we proposed a new framework for PAtient-centered treatment ranking via Large-scale Multivariate network meta-analysis, termed as PALM, which includes a parsimonious modeling approach, a fast algorithm for parameter estimation and inference, a novel visualization tool for comparing treatments with multivariate outcomes termed as the star plot, as well as personalized treatment ranking procedures taking into account the individual’s considerations on multiple outcomes. In application to an NMA that compares 14 treatment options for labor induction over five modalities, we provided a comprehensive illustration of the proposed framework and demonstrated its computational efficiency and practicality. Our analysis leads to new insights on comparing these 14 treatment options based on joint inference of multiple outcomes that cannot be obtained from univariate NMAs, and novel visualizations of evidence to support patient-centered clinical decision making.