TITLE: Group Sequential Design (GSDESIGN) and Non-Proportional Hazard
SPEAKERS: Keaven Anderson, Yilong Zhang and Xiao Nan, Merck & Co. Inc


We consider group sequential design for time-to-event endpoints under both proportional and non-proportional hazards assumptions for randomized clinical trials. While the primary focus will be on logrank testing due to its regulatory acceptance, weighted logrank test, combination tests and RMST will also be considered. Timing of analyses and boundary setting for efficacy and futility are critical topics to be discussed at length. A simple, piecewise model that can be used to approximate arbitrary scenarios is proposed. In addition to 2-arm comparisons for a single endpoint, we will also discuss graphical methods for strong control of Type I error when there are hypotheses for multiple endpoints and/or multiple populations. Asymptotic theory will be briefly noted as background, but the focus will be on applications, including software to quickly compare designs and scenarios. Throughout the course, we will develop designs incorporating each key new concept.

Speaker Bio:

Keaven Anderson, PhD, is an Associate Scientific VP of Methodology Research at Merck focused on late-stage statistical design and analysis. Keaven is a Fellow of the American Statistical Association. He has a long-standing interest in methodology, including survival analysis, group sequential design and multiplicity. He is the primary author of the gsDesign R package for group sequential design. While he has extensive experience in many therapeutic areas, his focus has been in oncology for the last 10+ years.





Yilong Zhang is a statistician from Merck. He is working with a group of statisticians and programmers to demonstrate the capability of using R for regulatory work. Other research interests include statistical methods in study design, missing data, and survival analysis. Before joining Merck, he earned Ph.D. degree in Biostatistics at New York University.




Nan Xiao (Ph.D. in statistics from Central South University, China). Nan is an Associate Principal Scientist in Methodology Research at Merck Research Laboratories. His research interests include sparse linear models, representation learning, and computational reproducibility. He received the John M. Chambers Statistical Software Award from the American Statistical Association in 2018. His current focus is on innovative design and analysis for clinical studies through statistical methods development and robust software implementation.

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