Survival Analysis Methods Correcting for Treatment Switching Effects in RCT

Survival Analysis Methods Correcting for Treatment Switching Effects in RCT

By popular request, our chapter is hosting an ASA Traveling Course this year! All will be welcome to participate and register for the course on November 6 at the University of Iowa.

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.

What will you learn from this course:

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

● Participants 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.

● Participants 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.

● Participants 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

Speakers:

Dr. Bingxia Wang is the Executive Director of Statistics and Quantitative Sciences in Data & Quantitative Science at Takeda. With more than 17 years of drug development experience, specializing in oncology, she is a recognized expert in the statistical design of oncology clinical trials and has played a pivotal role in regulatory submissions for high-impact oncology products. She frequently presents at statistical conferences and is passionate about sharing insights that support quantitative decision-making. She currently leads and contributes to multiple statistical research working groups, advancing methodologies in treatment switching and indirect treatment comparisons to support RWE/RWD generation. Dr. Wang holds a PhD in Biostatistics from Boston University.

Dr. Xuzhi Wang received his PhD in Biostatistics from Boston University and was a postdoctoral research fellow in the Department of Biostatistics at Harvard before joining Takeda, where he works on early‑ and late‑phase oncology trials. His research interests include treatment switching in oncology trials, innovative trial design, and joint modeling of longitudinal and survival data. Dr. Wang moved to industry last year and is a Manager of Statistics at Takeda.