Advancing Indirect Treatment Comparisons through Collective Statistical Innovation

Ensuring equitable access to safe, effective, and affordable health technologies is a shared priority central to universal health coverage, public health preparedness, and global community well-being. Achieving this objective requires not only biomedical advancements but also the coordinated contributions of statisticians, data scientists, and healthcare decision-makers who collectively generate and evaluate comparative evidence. Indirect treatment comparisons (ITCs) have become indispensable when head-to-head randomized controlled trials (RCTs) are infeasible, unethical, or unavailable. By synthesizing evidence across multiple studies—using approaches such as network meta-analysis, population-adjusted indirect comparisons (e.g., MAIC, STC), and emerging Bayesian and machine-learning–assisted methods—ITCs enable a comprehensive assessment of the relative benefits and risks of alternative healthcare interventions. These analyses require rigorous statistical reasoning, careful causal assumptions, and methodological innovation to address heterogeneity, bias, transportability, and data integration challenges. In alignment with the JSM 2026 theme, Communities in Action: Advancing Society, this proposed topic-contributed session will highlight how collaborative innovation across academia, industry, regulatory science, and health technology assessment (HTA) organizations is accelerating methodological progress in ITC. Speakers will present cutting-edge research, practical case studies, and forward-looking perspectives on: • Novel statistical and causal inference methods for ITC • Integration of individual-level and aggregate-level evidence • Bayesian and AI-enabled approaches to evidence synthesis • Solutions to real-world HTA challenges, including limited overlap, heterogeneity, and data quality • Cross-sector collaboration to advance transparency, reproducibility, and best practices By bringing together leaders from diverse sectors, this session exemplifies "communities in action"—showcasing how collective expertise and shared methodological advancements strengthen the credibility and societal impact of comparative effectiveness research. Attendees will gain both technical knowledge and a deeper understanding of how community-driven statistical innovation informs healthcare decision-making and promotes population-level benefit.