Quantile Regression for Left-Truncated Semicompeting Risks Data
Quantile Regression for Left-Truncated Semicompeting Risks Data
复制标题
DOI：
10.1111/j.1541-0420.2010.01521.x
复制
发表时间：
2011-09-01
期刊：
影响因子：
1.9
通讯作者：
Peng, Limin
中科院分区：
文献类型：
--
作者：
Li, Ruosha;Peng, Limin
在生物医学研究中，经常会遇到半抄写风险，在生物医学研究中，终止事件对非终止事件进行审查，但反之亦然。实际上，终止事件的左截断可能会出现，并且可能会使不终止事件的回归分析变得非常复杂。在这项工作中，我们提出了一种用于左截断的半票风险数据的分数回归方法，该方法提供了有意义的解释以及适应不同协变量效应的灵活性。我们开发估计和推理过程，可以通过现有统计软件轻松实施。确定所得估计量的渐近特性，包括均匀的一致性和弱收敛性。通过模拟研究评估了所提出方法的有限样本性能。注册表数据集的应用程序提供了我们建议的说明。
Semicompeting risks is often encountered in biomedical studies where a terminating event censors a nonterminating event but not vice versa. In practice, left truncation on the terminating event may arise and can greatly complicate the regression analysis on the nonterminating event. In this work, we propose a quantile regression method for left-truncated semicompeting risks data, which provides meaningful interpretations as well as the flexibility to accommodate varying covariate effects. We develop estimation and inference procedures that can be easily implemented by existing statistical software. Asymptotic properties of the resulting estimators are established including uniform consistency and weak convergence. The finite-sample performance of the proposed method is evaluated via simulation studies. An application to a registry dataset provides an illustration of our proposals.