A Semiparamtric Estimation Approach for Dealing with Treatment Switching in Randomized Oncology Trials

• Activity Number: 293
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Jin Zhang*+ and Cong Chen
• Companies: Merck and Merck
• Keywords: Oncology trial ; Overall Survival ; Treatment switching ; Non-compliance

Abstract

Patients randomized to the control arm are often allowed to cross over to the treatment arm after disease progression in randomized Phase III oncology trials, resulting in an underestimation of the treatment effect on overall survival as per the intent-to-treat approach. The Rank-Preserving Structural Failure Time Model (RPSFTM) has been widely used to adjust for the treatment switching with the advantage of respecting the randomization. However, the test statistics used in RPSFTM is very discrete with potentially multiple roots. Hence, it poses a great challenge to estimate the treatment effect and its variance. We adopted an approximate profile likelihood approach by using kernel smoothing in the accelerated failure time model to provide a computationally feasible procedure. We illustrate our method in a simulation study and a real Phase III oncology trial.