Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.

中文翻译：

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使用校准数据在协变量中混合 Berkson 和经典误差的 Cox 回归模型的模拟外推方法

许多生物医学或流行病学研究通常旨在评估关注事件的时间与 Cox 比例风险模型下的一些协变量之间的关联。然而，一个问题是协变量数据通常会涉及测量误差，其可能是经典类型、Berkson类型或这两种类型的组合。具有易错协变量的 Cox 回归问题已在统计文献中得到充分讨论，到目前为止，统计文献主要关注经典误差。当某些协变量可能受到伯克森误差和经典误差的混合污染时，本文考虑使用 Cox 回归分析。当误测协变量的两个重复项以及仅子样本中某些受试者的校准数据可用时，我们提出了一种基于模拟外推法的方法来解决此问题。所提出的方法没有对混合百分比做出假设。通过模拟研究评估其有限样本性能。它应用于艾滋病临床试验研究数据的分析。