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Activity Number: 178
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #319431
Title: Correcting Treatment Effect for Treatment Switching in Randomized Oncology Trials with a Generalized Rank-Preserving Structural Failure Time Model
Author(s): Jin Zhang* and Cong Chen
Companies: Merck and Merck
Keywords: Oncology ; Overall Survival
Abstract:

In randomized oncology trials, patients in the control arm are sometimes permitted to switch to receive experimental drug after disease progression. This is mainly due to ethical reasons or to reduce the patient dropout rate. While progression-free survival is not usually impacted by crossover, the treatment effect on overall survival can be highly confounded. The Rank-Preserving Structural Failure Time (RPSFT) model is one of the main randomization-based methods used to adjust for confounding in the analysis of overall survival. While the RPSFT has been extensively studied, one major assumption made is that the treatment effect is constant regardless of when treatment switching occurs. In practice, this assumption is difficult to verify and sometimes not valid. In this manuscript, we generalized the RPSFT to accommodate the scenario where the treatment effect changes after switching. We compared the RPSFT and the generalized RPSFT via extensive simulations and then walked through the analysis using the generalized RPSFT in a real clinical trial.


Authors who are presenting talks have a * after their name.

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