Semi-competing risks data frequently arise in biomedical studies when time to a disease landmark event is subject to dependent censoring by death, the observation of which however is not precluded by the occurrence of the landmark event. In observational studies, the analysis of such data can be further complicated by left truncation. In this work, we study a varying co-efficient subdistribution regression model for left-truncated semi-competing risks data. Our method appropriately accounts for the specifical truncation and censoring features of the data, and moreover has the flexibility to accommodate potentially varying covariate effects. The proposed method can be easily implemented and the resulting estimators are shown to have nice asymptotic properties. We also present inference, such as Kolmogorov-Smirnov type and Cramér Von-Mises type hypothesis testing procedures for the covariate effects. Simulation studies and an application to the Denmark diabetes registry demonstrate good finite-sample performance and practical utility of the proposed method.

中文翻译：

---

左截断的半竞争风险数据的变系数子分布回归

半竞争风险数据经常出现在生物医学研究中，当疾病标志性事件的时间受到死亡的依赖审查时，但标志性事件的发生并不排除对死亡的观察。在观察性研究中，左截断会使此类数据的分析进一步复杂化。在这项工作中，我们研究了左截断的半竞争风险数据的不同系数子分布回归模型。我们的方法适当地考虑了数据的特定截断和删失特征，而且还具有适应潜在变化协变量效应的灵活性。所提出的方法可以很容易地实现，并且由此产生的估计量被证明具有很好的渐近特性。我们还提出了推论，例如用于协变量效应的 Kolmogorov-Smirnov 型和 Cramér Von-Mises 型假设检验程序。模拟研究和在丹麦糖尿病登记处的应用证明了所提出方法的良好有限样本性能和实际效用。