PS44: Target Trial Emulation for Regulatory-Grade Real-World Evidence Generation in Rare Diseases and Oncology: Case Studies and An Expert Panel Discussion

Target Trial Emulation (TTE) has emerged as a transformative framework for generating regulatory-grade real-world evidence (RWE), bridging the gap between observational data and causal inference. Originally conceptualized to mimic randomized controlled trials (RCTs) using real-world data (RWD), TTE now offers a structured approach to address confounding, immortal time bias, and other challenges inherent in non-randomized studies. Its adoption is accelerating in regulatory science, particularly for rare diseases where traditional trials are often infeasible due to small patient populations and ethical constraints. Recent advances in methodology-such as cloning and censoring strategies, sequential trial emulation, and targeted learning algorithms-combined with digital innovations and dedicated software platforms have made TTE more accessible and robust. However, successful implementation still demands careful consideration of the research question, eligibility criteria, treatment strategies, and estimands, as well as transparent reporting aligned with regulatory expectations. This session will showcase how TTE can be applied to evaluate long-term outcomes of drug products using RWD, with a focus on oncology and rare disease contexts. It will feature two brief presentations - a case study in oncology illustrating data fitness, design choices, analytic workflows, and interpretation of results, and, a commentary from a regulator on potential limitations of TTE. Following these brief presentations, a multidisciplinary panel of experts from industry, academia, and the FDA will discuss: • Methodological breakthroughs: cloning and censoring, sequential emulation, targeted learning • Regulatory considerations: defining "regulatory-grade" RWE and aligning with FDA guidance • Data challenges: handling missingness, heterogeneity, and bias in rare disease, oncology datasets • Digital transformation: leveraging AI and automation to scale evidence generation and improve reproducibility The discussion will also explore future opportunities, including the integration of machine learning and AI for automated trial emulation and the role of digital health technologies in accelerating RWE generation. Attendees will leave with actionable insights on implementing TTE in practice, understanding its regulatory implications, and anticipating its evolution in the era of AI-driven analytics. This transdisciplinary session will feature expert speakers and panelists from diverse backgrounds including: • Laura Fernandes, PhD, Senior Statistical Director, Verana Health • Ben Ackerman, PhD, Director, Real-World Biostatistics, GSK plc • Shirley Wang, PhD, Associate Professor, Harvard Medical School • Hana Lee, PhD, Food and Drug Administration (FDA/CDER)