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

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, four complex statistical methods ( MSM, TSE, IPCW and RPSFTM) have been adapted and applied to RCTs to recover the causal OS effect of randomized active intervention under settings that allow for treatment switching. This course will review theory and demonstrate SAS/R code for these four 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. The short course is the first of its kind. It will cover four complex methods in both 1-way crossover and 2-way treatment switching settings in RCTs. These four methods can be divided into two different classes: TSE and RPSFTM belong to the rescaling based methods; MSM and IPCW belong to the reweighting based methods.