Ning Jing, Pak Daewoo, Zhu Hong, Qin Jing
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Division of Data Science, Yonsei University, Wonju 26493, Korea.
Comput Stat Data Anal. 2022 Apr;168. doi: 10.1016/j.csda.2021.107402. Epub 2021 Nov 19.
Conditional independence assumption of truncation and failure times conditioning on covariates is a fundamental and common assumption in the regression analysis of left-truncated and right-censored data. Testing for this assumption is essential to ensure the correct inference on the failure time, but this has often been overlooked in the literature. With consideration of challenges caused by left truncation and right censoring, tests for this conditional independence assumption are developed in which the generalized odds ratio derived from a Cox proportional hazards model on the failure time and the concept of Kendall's tau are combined. Except for the Cox proportional hazards model, no additional model assumptions are imposed, and the distributions of the truncation time and conditioning variables are unspecified. The asymptotic properties of the test statistic are established and an easy implementation for obtaining its distribution is developed. The performance of the proposed test has been evaluated through simulation studies and two real studies.
截断时间和失效时间在协变量条件下的条件独立性假设是左截断和右删失数据回归分析中的一个基本且常见的假设。检验这个假设对于确保对失效时间进行正确推断至关重要，但在文献中这一点常常被忽视。考虑到左截断和右删失所带来的挑战，开发了针对这个条件独立性假设的检验方法，该方法将基于失效时间的Cox比例风险模型导出的广义优势比与肯德尔tau的概念相结合。除了Cox比例风险模型外，不施加额外的模型假设，截断时间和条件变量的分布未作具体规定。建立了检验统计量的渐近性质，并开发了一种易于实现的方法来获得其分布。通过模拟研究和两项实际研究对所提出检验的性能进行了评估。
