Dr. Haito Chu presents: Bridging Evidence Gaps in Health Technology Assessment: The Strategic Role of Indirect Treatment Comparisons
Abstract: Health technology assessment (HTA) increasingly depends on indirect treatment comparisons (ITCs) to bridge evidence gaps when head‑to‑head clinical trials are unavailable. The European Union’s Joint Clinical Assessment (JCA) has further elevated the strategic importance of ITCs, highlighting the need for statistically sound methods that can support robust, transparent, and policy‑relevant decision making.
In this talk, we review established ITC approaches, including Bucher’s adjusted indirect comparison and network meta‑analysis (NMA), and examine the expanding role of population‑adjusted indirect comparisons (PAICs) in HTA. Key PAIC methods include matching‑adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). MAIC reweights individual participant data (IPD) to align covariate distributions with aggregate data (AgD), while STC applies outcome‑regression models to enable comparisons in the AgD target population.
Despite their value in addressing cross‑trial heterogeneity, PAICs are susceptible to the “PAIC paradox,” in which conclusions may depend on whether IPD or AgD defines the reference population. We conclude by discussing three foundational principles, transportability, collapsibility, and transitivity, that together provide a foundation for generating credible evidence and effectively bridging evidence gaps in HTA.
Dr. Haitao Chu, a distinguished biostatistician, currently holds the position of Senior Director at Pfizer's Statistical Research and Data Science Center. He earned his doctoral degree in Biostatistics from Emory University. Throughout his career, Dr. Chu has held various academic positions, including serving as an Assistant Professor of Epidemiology at Johns Hopkins University, a Research Associate Professor of Biostatistics at the University of North Carolina at Chapel Hill, and most recently, a tenured full Professor of Biostatistics at the University of Minnesota Twin Cities since 2017. With over 290 peer-reviewed publications and more than 20,000 citations on Google Scholar, Dr. Chu's contributions to the field of biostatistics have been widely recognized. In 2016, he was elected as a Fellow of the American Statistical Association.