Department of Biostatistics, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
Staburo GmbH, Munich, Germany.
Sci Rep. 2023 Aug 18;13(1):13477. doi: 10.1038/s41598-023-40538-2.
A randomized controlled trial is commonly designed to assess the treatment effect in survival studies, in which patients are randomly assigned to the standard or the experimental treatment group. Upon disease progression, patients who have been randomized to standard treatment are allowed to switch to the experimental treatment. Treatment switching in a randomized controlled trial refers to a situation in which patients switch from their randomized treatment to another treatment. Often, the switchis from the control group to the experimental treatment. In this case, the treatment effect estimate is adjusted using either convenient naive methods such as intention-to-treat, per-protocol or advanced methods such as rank preserving structural failure time (RPSFT) models. In previous simulation studies performed so far, there was only one possible outcome for patients. However, in oncology in particular, multiple outcomes are potentially possible. These outcomes are called competing risks. This aspect has not been considered in previous studies when determining the effect of a treatment in the presence of noncompliance. This study aimed to extend the RPSFT method using a two-dimensional G-estimation in the presence of competing risks. The RPSFT method was extended for two events, the event of interest and the competing event. For this purpose, the RPSFT method was applied based on the cause-specific hazard approach, the result of which is compared to the naive methods used in simulation studies. The results show that the proposed method has a good performance compared to other methods.
一项随机对照试验通常用于评估生存研究中的治疗效果，在该研究中，患者被随机分配到标准治疗组或实验治疗组。在疾病进展后，随机分配到标准治疗的患者可以被允许转为实验治疗。随机对照试验中的治疗转换是指患者从随机治疗转为另一种治疗的情况。通常，这种转换是从对照组到实验组。在这种情况下，治疗效果估计是通过方便的朴素方法（如意向治疗、方案治疗或高级方法如等级保持结构失效时间（RPSFT）模型）进行调整。在迄今为止进行的模拟研究中，患者只有一种可能的结果。然而，在肿瘤学中，特别是，可能有多种结果。这些结果被称为竞争风险。在考虑到不依从性的情况下，这方面在以前的研究中并没有被考虑到。本研究旨在扩展存在竞争风险时使用二维 G 估计的 RPSFT 方法。RPSFT 方法扩展到两个事件，即感兴趣的事件和竞争事件。为此，基于特定原因的危险方法应用 RPSFT 方法，其结果与模拟研究中使用的朴素方法进行比较。结果表明，与其他方法相比，所提出的方法具有良好的性能。
