Connecting the Dots in Autoimmune Disease HTA: Practical Strategies and Future Opportunities in ITC, Modeling, and RWE
Moderator
Shilpi Swami, ConnectHEOR, London, United Kingdom
Speakers
Susannah Sadler, MSc, ConnectHEOR, United Kingdom; Shijie Ren, PhD, ConnectHEOR, London, United Kingdom; Tushar Srivastava, MSc, ConnectHEOR Limited, London, United Kingdom
Autoimmune diseases present unique challenges for health technology assessment (HTA) submissions. Complex disease courses, frequent treatment switching, composite endpoints, and heterogeneous patient populations mean that conventional evidence strategies often fall short. As a result, sponsors frequently face HTA rejections or requests for further clarification when their evidence packages rely on weak or fragmented analyses. This session will deliver practical, solution-oriented insights into current best practice, and explore some familiar evidence gaps with ideas and discussion of novel solutions. The discussion will span indirect treatment comparisons (ITCs), economic modeling, and real-world evidence (RWE), highlighting how these elements can be better aligned to meet HTA expectations. Speakers will explore: (1) How autoimmune ITCs differ from other therapy areas, and how to avoid common pitfalls in synthesis methods. (2) Why long-term modeling and treatment sequencing require flexible structures and careful management of uncertainty. (3) How RWE can be planned early and integrated seamlessly with trial and model evidence - rather than applied as an afterthought. Attendees will gain a set of practical frameworks to strengthen their autoimmune disease evidence submissions, supported by real case examples. This session is not only designed to be a go-to resource for sponsors, HTA strategists, and payers looking for actionable approaches to evidence generation in autoimmune diseases, but also to prompt thought leaders in the sphere with some potential novel solutions.
Sponsored by connectHEOR
Code
034
Topic
Health Technology Assessment, Methodological & Statistical Research, Real World Data & Information Systems