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Abstract:
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In oncology clinical trials, characterizing the long-term overall benefit for an experimental drug or treatment regimen is often unobservable if some patients in the control group switch to drugs in the experimental group after disease progression. A key question often raised by payers and reimbursement agencies is how to estimate the true benefit of the treatment on overall survival if there were no treatment switches. Several commonly used statistical methods are currently available, ranging from naive exclusion or censoring approaches to more advanced methods including inverse probability of censoring weighting, iterative parameter estimation 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.
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