PS29: Indirect Treatment Comparisons for Impactful Transdisciplinary Decision-making

Access to high-quality, affordable, and appropriate health technologies is fundamental to achieving universal health coverage, responding effectively to public health emergencies, and improving population health outcomes. Central to these objectives is the rigorous evaluation of alternative treatment options, which forms the backbone of comparative effectiveness research (CER) and health technology assessment (HTA). These evaluations directly inform high-stakes decisions made by regulators, payers, clinicians, and policymakers, often in settings characterized by uncertainty, evolving evidence, and pressing time constraints. In many therapeutic areas, direct head-to-head randomized controlled trials (RCTs) are unavailable or infeasible due to ethical, practical, or logistical considerations. In such contexts, indirect treatment comparisons (ITC) serve as an essential methodological framework for enabling evidence-based decision-making. ITC approaches, including network meta-analysis and population-adjusted indirect comparisons, allow investigators to synthesize evidence across complex networks of studies by integrating both direct comparisons and indirect evidence connected through common comparators. These methods support decision-making across diverse clinical and policy settings, where stakeholders must weigh relative benefits and risks among multiple competing interventions. Modern ITC increasingly draws on both aggregate-level trial data and individual patient-level data, enabling more nuanced assessment of treatment effect heterogeneity and population relevance. Advances in statistics, causal inference, and computational methods have expanded the methodological toolbox for ITC, allowing for more transparent handling of assumptions such as similarity, exchangeability, and consistency. In parallel, the rapid evolution of digital health data infrastructures has introduced new opportunities and challenges for integrating real-world evidence into comparative effectiveness analyses. Within this evolving digital era, artificial intelligence (AI) and machine learning (ML) are playing an increasingly influential role in ITC research. AI-driven tools facilitate scalable data integration, automated evidence extraction, and flexible modeling strategies that can accommodate high-dimensional covariates and complex outcome structures. When combined with principled statistical frameworks, these technologies have the potential to enhance both the precision and interpretability of ITC results, while improving the efficiency and transparency of evidence generation. Aligned with the 2026 RISW theme, "Impactful Transdisciplinary Decision-making in the Evolving Digital Era," this session will explore how methodological innovation in ITC is reshaping healthcare decision-making across disciplinary boundaries. Three distinguished experts from academia and the pharmaceutical industry will present perspectives on emerging statistical methodologies, AI-enabled analytics, and integrated evidence frameworks that bridge methodological rigor with real-world decision needs. Specifically, Dr. Weili He will present a novel approach named "arbitrated indirect treatment comparisons" focusing on ITCs conducted by decision-maker or independent body instead of sponsors. Dr. Joseph C Cappelleri will present an extensive simulation study evaluating the performance of unanchored population-adjusted indirect comparison methods in small sample size settings. Lastly, Dr. Haitao Chu will discuss the assumptions underlying credible indirect treatment comparisons, including study similarity, transitivity across treatment contrasts, consistency of direct and indirect evidence, and transportability of treatment effects to the target population for decision-making. The overarching goal of this session is to highlight state-of-the-art ITC methods that enhance credibility, transparency, and decision relevance, while fostering dialogue on the opportunities, limitations, and future directions of ITC in a data-rich, digitally connected healthcare ecosystem.