Fri, Jan 16, 2026, 08:00 – 09:00AM (PDT), 11:00 – 12:00 noon (EST)
Title:
Ensuring Quality and Interpretability of Progression Free Survival and Overall Survival in Oncology Clinical Trials
Abstract:
Time-to-event endpoints, such as progression free survival (PFS) and overall survival (OS), are critical in assessing therapeutic efficacy in oncology drug development. However, their quality and interpretability are frequently challenged by a range of factors, from protocol design and intercurrent events (ICE) to inconsistent data collection and missing follow-up data. These methodological and operational complexities can obscure the true treatment effect. Discontinuation of study treatment, initiation of subsequent anticancer therapy, lost to follow-up and withdrawal of consent can introduce significant bias, limiting the robustness of survival endpoints and complicating regulatory decision making. Adopting a prospective ICH E9(R1) estimand framework helps mitigate risks associated with data collection, analysis methodology and interpretability. This facilitates clearer discussions with regulators and stakeholders. Although both the FDA guidance on oncology endpoints and the EMA guideline on anticancer medicinal product evaluation outline key principles in evaluating PFS and OS endpoints, integration of ICH E9(R1) offers a harmonized strategy that is important for the design and conduct of randomized late phase oncology clinical trials. In this article, we investigate the quality and interpretability of the endpoints of PFS and OS according to the ICH E9(R1) framework and present some practical recommendations for designing and conducting robust oncology clinical trials. This topic is presented on behalf of the IDSWG Oncology WG (https://oncologytrialdesign.org/).
Reference:
He P, Ma H, Lu CC, et al. Ensuring Quality and Interpretability of Progression Free Survival and Overall Survival in Oncology Clinical Trials. Ther Innov Regul Sci. 2025;59(6):1495-1505. doi:10.1007/s43441-025-00848-1
Bio:
Philip He, PhD, is a statistician at Daiichi Sankyo, Inc., with two decades of experience in oncology drug development, spanning early-phase trials through regulatory approvals. He has led statistical teams supporting successful development of multiple marketed cancer therapies, including chemotherapy, tyrosine kinase inhibitors, monoclonal antibodies, and immunotherapies. Passionate about advancing oncology clinical development, Dr. He actively contributes to the scientific community through volunteer service in professional organizations, editorial and peer-review work, and conference organization. He has published on topics such as adaptive designs, estimands, dose optimization, and Bayesian statistics. Dr. He currently serves as Head of Early Phase Statistics at Daiichi Sankyo and co-chairs the DahShu Innovative Design and Scientific Working Group (IDSWG) Oncology Team (https://oncologytrialdesign.org/) to collectively advance innovative trial designs.
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