Abstract
OBJECTIVES: To compare the commonly used methods for analyzing treatment switching in clinical trials to facilitate selection of optimal methods in different scenarios. METHODS: Based on the data characteristics of patient conversion in oncology clinical trials, we simulated the survival time of patients across different scenarios and compared the bias, mean square error and coverages of the treatment effects derived from different methods. RESULTS: The sample size had an almost negligible impact on the outcomes of the various methods. Compared to conventional methods, more complex methods (RPSFTM, IPCW, TSE, and IPE) resulted in lower errors across different scenarios. The IPCW method could cause a significant increase in errors in cases where the probability of conversion was high. The TSE method had the lowest error and mean squared error when the risk was low and the probability of conversion was high. The IPE method had an obvious advantage in the scenario with a low probability of conversion, but it may slightly underestimate the treatment effect when the inflation factor was small. CONCLUSIONS: The choice of a specific method for analyzing cohort transition should be made based on considerations of both the probability of conversion and inflation factor in different scenarios.