Survival Analysis Methods Correcting for Treatment Switching Effects in RCT

Instructors:

Jing Xu, Senior Director, Takeda

Bingxia Wang, Senior Director, Takeda

Qingxia Chen, Professor, Department of Biostatistics, University of Vanderbilt Medical Center

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 course will 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.

For pre-readings, slide decks for the course will be distributed to registered attendees through emails 3 days before the course date by the course organizer.

Course learning objectives:

• Attendees will be familiar with available methods, regulatory policy, and appropriate approaches in dealing with issues associated with treatment switching.
• Attendees will be familiar with the basic ideas, strengths, and limitations, as well as practical issues related to the application of these complex methods in RCTs under one-way crossover and 2-way treatment switching settings.
• Attendees 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.
• Attendees will be able to construct longitudinal counting process style datasets in SAS for adjusted analyses 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.

About the instructors

Dr. Jing Xu is a senior director of biostatistics at Takeda. He joined Millennium (took over by Takeda in 2008) in 2006 and was a lead statistician in the Entyvio program until 2015. 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 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 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 currently serves as Vice Chair of Education, Department of Biostatistics; Director, Biostatistics Post-Graduate Studies and Distance Learning; Director, Executive Data Science Program Departments of Biostatistics and Biomedical Informatics, and Professor of Biostatistics, Biomedical Informatics, and Ophthalmology & Visual Sciences at University of Vanderbilt Medical Center. She holds a PhD degree in biostatistics from University of North Carolina at Chapel Hill.

Detailed program for the whole day short course with four modules: