  In oncology clinical trials, characterizing the long-term overall survival
(OS) benefit for an experimental drug or treatment regimen (experimental group)
is often unobservable if some patients in the control group switch to drugs in
the experimental group and/or other cancer treatments after disease
progression. A key question often raised by payers and reimbursement agencies
is how to estimate the true benefit of the experimental drug group on overall
survival that would have been estimated if there were no treatment switches.
Several commonly used statistical methods are available to estimate overall
survival benefit while adjusting for treatment switching, ranging from naive
exclusion or censoring approaches to more advanced methods including inverse
probability of censoring weighting (IPCW), iterative parameter estimation (IPE)
algorithm or rank-preserving structural failure time models (RPSFTM). However,
many clinical trials now have patients switching to different treatment
regimens other than the test drugs, and the existing methods cannot handle more
complicated scenarios. To address this challenge, we propose two additional
methods: stratified RPSFTM and random-forest-based prediction. A simulation
study is conducted to assess the properties of the existing methods along with
the two newly proposed approaches.
