随机对照试验通常用于评估生存研究中的治疗效果，其中患者被随机分配到标准治疗组或实验治疗组。随着疾病进展，被随机分配到标准治疗组的患者可以转换到实验治疗。在随机对照试验中，治疗转换是指患者从随机治疗转换为其他治疗的情况。通常情况下，转换是从对照组到实验治疗。在这种情况下，治疗效果的估计需要使用方便的幼稚方法，如意向治疗法、按计划分析法，或者使用更先进的方法，如保序结构失效时间模型。到目前为止，以往的模拟研究只有一种可能的结果，但在特定的肿瘤学中，存在多种潜在结果，这些结果被称为竞争风险。以前的研究在确定不服从情况下治疗效果时未考虑这一方面。本研究旨在在存在竞争风险的情况下扩展保序结构失效时间方法，使用二维G估计方法。保序结构失效时间方法被扩展用于两个事件，即感兴趣事件和竞争事件。为此，基于特定原因风险的方法应用了保序结构失效时间方法，其结果与模拟研究中使用的幼稚方法进行了比较。研究结果显示，提出的方法与其他方法相比表现良好。 © 2023. Springer Nature Limited.

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.© 2023. Springer Nature Limited.