Zeng Donglin, Chen Qingxia, Chen Ming-Hui, Ibrahim Joseph G
Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, North Carolina 27516, U.S.A.,
Biometrika. 2012 Mar;99(1):167-184. doi: 10.1093/biomet/asr062. Epub 2011 Dec 29.
Treatment switching is a frequent occurrence in clinical trials, where, during the course of the trial, patients who fail on the control treatment may change to the experimental treatment. Analysing the data without accounting for switching yields highly biased and inefficient estimates of the treatment effect. In this paper, we propose a novel class of semiparametric semicompeting risks transition survival models to accommodate treatment switches. Theoretical properties of the proposed model are examined and an efficient expectation-maximization algorithm is derived for obtaining the maximum likelihood estimates. Simulation studies are conducted to demonstrate the superiority of the model compared with the intent-to-treat analysis and other methods proposed in the literature. The proposed method is applied to data from a colorectal cancer clinical trial.
治疗方案切换在临床试验中经常发生，即在试验过程中，对照治疗失败的患者可能会转而接受试验性治疗。在不考虑切换的情况下分析数据会产生对治疗效果的高度有偏且低效的估计。在本文中，我们提出了一类新颖的半参数半竞争风险转移生存模型来适应治疗方案的切换。研究了所提出模型的理论性质，并推导了一种有效的期望最大化算法以获得最大似然估计。进行了模拟研究以证明该模型相对于意向性分析和文献中提出的其他方法的优越性。所提出的方法应用于一项结直肠癌临床试验的数据。
