Length-biased data are frequently encountered in prevalent cohort studies. Many statistical methods have been developed to estimate the covariate effects on the survival outcomes arising from such data while properly adjusting for length-biased sampling. Among them, regression methods based on the proportional hazards model have been widely adopted. However, little work has focused on checking the proportional hazards model assumptions with length-biased data, which is essential to ensure the validity of inference. In this article, we propose a statistical tool for testing the assumed functional form of covariates and the proportional hazards assumption graphically and analytically under the setting of length-biased sampling, through a general class of multiparameter stochastic processes. The finite sample performance is examined through simulation studies, and the proposed methods are illustrated with the data from a cohort study of dementia in Canada.

中文翻译：

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具有长度偏向数据的比例风险模型的模型诊断。

在流行的队列研究中经常会遇到长度偏倚的数据。已经开发出许多统计方法来估计由此类数据对生存结果的协变量影响，同时适当地调整长度偏倚的采样。其中，基于比例风险模型的回归方法已被广泛采用。但是，很少有工作集中在用长度偏倚的数据检查比例风险模型假设上，这对于确保推理的有效性至关重要。在本文中，我们提出了一种统计工具，用于通过一般类的多参数随机过程，在长度偏倚的采样设置下，以图形和分析方式测试协变量的假定功能形式和比例风险假设。