Session (CTI)

PURPOSE: Traditional network meta-analyses (NMA) typically focus on single outcomes like progression-free survival, yet clinical decision-making inherently requires balancing multiple endpoints simultaneously. This workshop brings together diverse perspectives to discuss innovative methods and the promising potential of multivariate evidence synthesis. How can multivariate approaches bridge otherwise disconnected treatment networks allowing for broader treatment comparisons? Can we jointly synthesize progression-free and overall survival through multi-state modeling for improved economic modelling? Can multivariate methods enable a more patient-centered treatment ranking by considering efficacy and safety related outcomes simultaneously? There are challenges to overcome and we address the complexities of between-outcome and between-comparison correlations, how to leverage these correlations to improve the precision of treatment effect estimates, and how to provide more comprehensive evidence that better reflects the multifaceted nature of clinical decision-making. DESCRIPTION: Attendees will gain practical insights into when and how to implement multivariate methods, understand their advantages over univariate analyses, and learn about emerging tools for the visualization of complex multi-outcome evidence. Dr. Williams will chair the session and begin by summarizing current practice (8 min). Dr. Campbell will then review how different multivariate NMA models can be used to bridge otherwise disconnected networks (15 min). Dr. Jansen will explain multi-state NMA of progression-free- and overall survival that is based on a Markov state-transition model where time-varying transition rates and relative treatment effects are modeled using aggregate-level data (15min). Finally, Dr. Chen will consider a new framework for PAtient-centered treatment ranking via Large-scale Multivariate NMA, termed as PALM (15 min). This workshop will engage attendees with real-time polling and structured discussion, leveraging the different perspectives of the panel to probe how multivariate methods can ultimately improve clinical decision-making.