Bias and Type I error Control in Correcting Treatment Effect for Treatment Switching Using Marginal Structural Models in Phase III Oncology Trials.

This research focuses on the bias and type I error control issues when the marginal structural models (MSMs) are applied to evaluate the causal survival benefits of active intervention versus control in randomized clinical trials (RCTs) with treatment switching after disease progression. When MSMs are applied in the RCT setting, the question of interest, model specifications, strategies for type I error control, bias reduction, etc. differ somewhat from those for observational studies. This manuscript discusses the approaches used to accommodate these differences. Through Monte Carlo simulations and a case study, our research demonstrates that, with sufficient attention paid to issues applicable to RCTs in particular, MSMs may perform better than the inverse probability of censoring weighting (IPCW) method in analyzing the survival endpoint in RCTs with treatment switching because more information is used by the MSM.