Recent Developments and Innovative Methods for Indirect Treatment Comparisons in CER and HTA

Access to high-quality, affordable, and appropriate health products is a cornerstone for advancing universal health coverage, addressing health emergencies effectively, and promoting the overall well-being of populations. In the quest to achieve these goals, the evaluation of alternative treatment options plays a pivotal role in comparative effectiveness research (CER) and health technology assessment (HTA). When direct head-to-head randomized controlled trials (RCTs) are not feasible due to practical, ethical, or logistical constraints, indirect treatment comparisons (ITC) become a critical tool in healthcare decision making. ITC allows to assess and compare the relative effectiveness and safety of different treatment options by synthesizing evidence from various sources. Indirect treatment comparisons (including network meta-analysis and population-adjusted indirect comparisons) leverage both direct evidence from trials where treatments are directly compared and indirect evidence from trials where treatments are compared through a common comparator. This approach often involves the aggregation of study-level summary data and, when available, individual patient-level data. By combining these data sources, using non-standard and rigorous statistical techniques, ITC provides a comprehensive view of the relative benefits and risks associated with different health interventions. As the field of statistics, data science and artificial intelligence (AI) evolves, innovative methodologies and causal inference methods are emerging to enhance the accuracy and reliability of ITC. In line with the 2025 JSM theme, "Statistics, Data Science, and AI Enriching Society", this session is designed to explore emerging topics and innovative statistical methodologies that are shaping the future of ITC studies. Specifically, we will delve into the latest advancements in statistical techniques, data integration methods, and AI-driven approaches that are enhancing the design and analysis of ITC studies. By addressing these cutting-edge developments, we aim to improve the precision and applicability of ITC, ultimately leading to more informed and effective healthcare decisions. Equally importantly, the session is intended to raise awareness among the general statistical community about the challenges and opportunities of this important tool of evidence generation. World-renowned leaders and scientists from academia and the pharmaceutical industry will present and discuss the state-of-the-science approaches to drive innovation in this area of profound impact. Specifically, Dr. Weili He from Abbvie will discuss some of the challenges associated with the existing approaches, with particular emphasis on innovative approaches in handling missing-data in the application of matching-adjusted indirect comparison. Dr. Joseph Cappelleri from Pfizer, who has published extensively on the topic, will present a network meta-interpolation approach adjusting effect modification in network meta-analysis using subgroup analyses. Thirdly, Dr. Lifeng will present a novel procedure involving nonparametric Bayesian approach to treatment ranking in network meta-analysis. Finally, Dr. Demissie Alemayehu, will provide a critical review of the papers in relation to their importance in comparative effectiveness research and health technology assessment.