A multiple imputation approach in enhancing causal inference for overall survival in randomized controlled trials with crossover

Crossover or treatment-switching in randomized controlled trials presents notable challenges not only in the development and approval of new drugs but also poses a complex issue in their reimbursement, especially in oncology. When the investigational treatment is superior to control, crossover from control to investigational treatment upon disease progression or for other reasons will likely cause the underestimation of treatment benefit. Rank Preserving Structural Failure Time (RPSFT) and Two-Stage Estimation (TSE) methods are commonly employed to adjust for treatment switching by estimating counterfactual survival times. However, these methods may induce informative censoring by adjusting censoring times for switchers while leaving those for non-switchers unchanged. Existing approaches such as re-censoring or inverse probability of censoring weighting (IPCW) are often used alongside RPSFT or TSE to handle informative censoring, but may result in long-term information loss or suffer from model misspecification. In this paper, Kaplan-Meier multiple imputation with bootstrap procedure (KMIB) is proposed to address the informative censoring issues in adjustment methods for treatment switching. This approach can avoid information loss and is robust to model misspecification. In the scenarios that we investigate, simulation studies show that this approach performs better than other adjustment methods when the treatment effect is small, and behave similarly under other scenarios despite different switching probability. A case study in non-small cell lung cancer (NSCLC) is also provided to demonstrate the use of this method.