Liang Zhiyue, Xu Lishan, Li Keke, Yu Milai, An Shengli
Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2025 May 20;45(5):1093-1102. doi: 10.12122/j.issn.1673-4254.2025.05.23.
To compare the commonly used methods for analyzing treatment switching in clinical trials to facilitate selection of optimal methods in different scenarios.
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.
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.
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.
比较临床试验中分析治疗转换的常用方法，以便在不同情况下选择最佳方法。
基于肿瘤学临床试验中患者转换的数据特征，我们模拟了不同情况下患者的生存时间，并比较了不同方法得出的治疗效果的偏差、均方误差和覆盖率。
样本量对各种方法的结果影响几乎可以忽略不计。与传统方法相比，更复杂的方法（RPSFTM、IPCW、TSE和IPE）在不同情况下导致的误差更低。在转换概率较高的情况下，IPCW方法可能会导致误差显著增加。当风险较低且转换概率较高时，TSE方法的误差和均方误差最低。IPE方法在转换概率较低的情况下具有明显优势，但当膨胀因子较小时，可能会略微低估治疗效果。
应根据不同情况下的转换概率和膨胀因子来选择分析队列转换的具体方法。
