On an enhanced rank-preserving structural failure time model to handle treatment switch, crossover, and dropout

Abstract

It is very challenging to estimate the comparative treatment effect between a treatment therapy and a control therapy on overall survival in the presence of treatment crossover, switch to an alternative non-study therapy, and non-random patient dropout. Existing methods (e.g., intent-to-treat and per-protocol) are known to be biased. We proposed two new estimators to address these analytical challenges and evaluated their performance via a comprehensive simulation study. The new estimators were constructed by combining an enhanced rank-preserving structural failure time model and the inverse probability censoring weighting approach. In the simulation study, we assessed and compared the performance of the two new estimators with four estimators from existing methods. The simulation results show that the new estimators have much better performance in almost all considered settings compared with the existing estimators. Copyright © 2017 John Wiley & Sons, Ltd.

Keywords: dropout; inverse probability censoring weighting; rank preserving structural failure time model; treatment switch.