2025 ASA Traveling Short Course

“Survival Analysis Methods Correcting for Treatment Switching Effects in RCTs: Theory and SAS/R Code”

Instructors:

Dr. Jing Xu, Senior Director, Takeda

Dr. Bingxia Wang, Senior Director, Takeda

Dr. Qingxia (Cindy) Chen, Professor, Department of Biostatistics, Vanderbilt University

When: Friday, October 3rd, 2025, 9:00 am – 4:00 pm PDT (Sign-in at 8:45pm)

Where: Genentech Building 40 (Main Floor, Room B44 - 1B/C/D), 600 East Grand Avenue, South San Francisco, CA, 94080

(virtual option will be offered but same registration fees)

Registration site: link

The registration deadline is Sunday September 28th at 11:30pm PDT. On-site registration will NOT be available.

Course Description:

In many late phase oncology randomized controlled trials (RCTs), control arm patients are permitted to take active treatment (1-way crossover), or patients in both control and active arms are permitted to take alternative treatments (2-way treatment switching) after disease progression due to ethical considerations. In both situations, the effect of active intervention on overall survival (OS) is no longer directly observable. The intent-to-treat (ITT)analysis of the observed data will reflect the trial outcome per the treatment policy strategy but may not be able to make causal inference for the active intervention effect on OS. The latter is important for the payer agency's evaluation and is helpful for regulatory decisions on drug applications.

During the last decade, several complex statistical methods have been adapted and applied to RCTs to recover the causal OS effect of randomized active intervention under settings that allow for treatment switching. These methods include but are not limited to MSM, TSE, IPCW, RPSFTM, IPE, Three-State Model. This coursewill review theory, regulatory

guidance and demonstrate SAS/R code for these methods. It will discuss the pros and cons and practical issues when each method is applied under the RCT setting. Case studies will be presented to illustrate the application of each method.

What will you learn from this course:

●     Students will be familiar with available methods, regulatory policy, and appropriate approaches in dealing with issues associated with treatment switching.

●     Students will be familiar with the basic ideas, strengths, and limitations, as well as practical issuesrelated to the application of these complex methods in RCTs under one-way crossover and 2-way treatment switching settings.

●     Students will understand how to select appropriate adjusted analysis methods at the RCT design stage and pre-specify considerations for using the selected methods in statistical analysis plans.

●     Students will be able to construct longitudinal counting process style datasets in SAS for adjustedanalyses under different treatment switching settings; implement appropriate SAS/R code for these methods; and apply SAS macros generating weighted log-rank test and adjusted survival curves when needed.

Tentative Agenda:

8:45 - 9:00 am.               Sign-in

9:00 - 9:05 am Opening

9:05 - 10:25 am Introduction and MSM (part 1)

10:25 - 10:40 am Break

10:40 - 12:00 pm MSM (part 2) and IPCW

12:00 - 1:00 pm Lunch

1:00 - 2:15 pm TSE and RPSFTM

2:15 - 2:30 pm Break

2:30 - 4:00 pm Three-State Model

Registration Fee:

ASA members/SFASA Chapter members: $290 Students/Postdocs/Academia: $110

Non-members: $410

The registration fee includes light refreshments during breaks, and a lunch box.

About the Instructors:

Dr. Jing Xu is a senior director of biostatistics at Takeda. He joined Millennium (taken over by Takeda in 2008) in 2006 and was a lead statistician in the Entyvio program until2015. Then, he transferred and has been working on oncology projects. His current research interests include casual inference methods recovering treatment effect under hypothetical strategies. He finished his PhD training in biostatistics at Boston University.

Dr. Bingxia Wang currently serves as the Senior Director of Statistics and Quantitative Sciences at the Data & Quantitative Science department at Takeda. With over 15 years of experience in drug development, specializing in oncological disease areas, she has emerged as a leading expert in the statistical design of oncology clinical trials. She has played a pivotal role in shaping regulatory submissions for high-impact oncology products. She has presented on numerous topics at statistical conferences, where she loves to share her insights and knowledge to help inform quantitative decision-making processes. She is currently leading and participating in various statistical research working groups, contributing to new methodologies  for  treatment  switch  and  indirect  treatment

comparison for RWE/RWD generation. Bingxia holds a PhD degree in biostatistics from Boston University.

Dr. Qingxia (Cindy) Chen is a Professor of Biostatistics, Biomedical Informatics, and Ophthalmology & Visual Sciences at Vanderbilt University Medical Center. She also serves as the Vice Chair of Education at the Department of Biostatistics. Her statistical research currently centers around several key areas, including missing data, survivalanalysis, and Bayesian approaches. Additionally, she is dedicated to the development of statistical methods specifically designed to analyze multimodal data in precision medicine. Notably, she has been actively involved in the

All of Us Research Program, contributing her expertise to advance precision medicine initiatives.