Statistical Methods for Causal Effects in Oncology Trials with Treatment Switching: Advancement and Strategies

Music City Center 

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 included but not limited to Marginal Structural Model (MSM), Two-Stage Estimation (TSE), Inverse Probability of Censoring Weighting (IPCW), Rank Preserving Structure Failure Time Model (RPSFTM), etc. This talk will review these methods, regulatory guidance and strategies how to select appropriate adjusted analysis methods at the RCT design stage. Case studies will also be presented to illustrate the pros and cons along with practical issues when each method is applied under the RCT setting.

causal inference, treatment switch, MSM, IPCW, TSE, RPSFTM, IPE