In this work, we study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semi-competing risks setting, where time to censoring remains observable after the occurrence of the event of interest. While such a scenario frequently arises in biomedical studies, most of current quantile regression methods for censored data are not applicable because they generally require the censoring time and the event time be independent. By imposing rather mild assumptions on the association structure between the time-to-event response and the censoring time variable, we propose quantile regression procedures, which allow us to garner a comprehensive view of the covariate effects on the event time outcome as well as to examine the informativeness of censoring. An efficient and stable algorithm is provided for implementing the new method. We establish the asymptotic properties of the resulting estimators including uniform consistency and weak convergence. The theoretical development may serve as a useful template for addressing estimating settings that involve stochastic integrals. Extensive simulation studies suggest that the proposed method performs well with moderate sample sizes. We illustrate the practical utility of our proposals through an application to a bone marrow transplant trial.

中文翻译：

---

针对半竞争风险的相关审查进行分位数回归调整。


在这项工作中，我们研究当响应是受潜在相关审查影响的事件时间时的分位数回归。我们考虑半竞争风险设置，其中在感兴趣的事件发生后仍然可以观察到审查时间。虽然这种情况在生物医学研究中经常出现，但目前大多数用于审查数据的分位数回归方法并不适用，因为它们通常要求审查时间和事件时间是独立的。通过对事件时间响应和审查时间变量之间的关联结构施加相当温和的假设，我们提出了分位数回归程序，这使我们能够全面了解协变量对事件时间结果的影响，并检查审查的信息量。为新方法的实现提供了一种高效稳定的算法。我们建立了所得估计量的渐近性质，包括一致一致性和弱收敛性。理论发展可以作为解决涉及随机积分的估计设置的有用模板。广泛的模拟研究表明，所提出的方法在适度的样本量下表现良好。我们通过骨髓移植试验的应用来说明我们的建议的实际用途。