Accelerated failure time models with covariates subject to measurement error
Accelerated failure time models with covariates subject to measurement error
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DOI：
10.1002/sim.2892
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发表时间：
2007-11-20
影响因子：
2
通讯作者：
Xiong, Juan
中科院分区：
文献类型：
--
作者：
He, Wenqing;Yi, Grace Y.;Xiong, Juan
众所周知，在包括线性和非线性回归在内的许多情况下，忽略测量误差可能导致实质上有偏的估计。对于协变量中存在测量误差的生存数据，文献中已进行了广泛讨论，重点是考克斯比例风险模型。然而，测量误差对加速失效时间（AFT）模型的影响却很少受到关注，尽管AFT模型在生存数据分析中非常有用。本文讨论了具有易错协变量的AFT模型，并研究了忽略协变量中测量误差的朴素方法所引起的偏差。为了调整这样的偏差，我们描述了一种模拟和外推方法。这种方法是有吸引力的，因为它是简单的实现，它不需要建模的真实，但容易出错的协变量的过程，往往是不可观察的。渐近正态估计的建立。仿真研究进行了评估所提出的方法的性能以及忽略测量误差的协变量的影响。所提出的方法被应用到分析所产生的数据集的布塞尔顿健康研究。
It has been well known that ignoring measurement error may result in substantially biased estimates in many contexts including linear and nonlinear regressions. For survival data with measurement error in covariates there has been extensive discussion in the literature with the focus being on the Cox proportional hazards models. However, the impact of measurement error on accelerated failure time (AFT) models has received little attention, though AFT models are very useful in survival data analysis. In this paper, we discuss AFT models with error-prone covariates and study the bias induced by the naive approach of ignoring measurement error in covariates. To adjust for such a bias, we describe a simulation and extrapolation method. This method is appealing because it is simple to implement and it does not require modelling the true but error-prone covariate process that is often not observable. Asymptotic normality for the resulting estimators is established. Simulation studies are carried out to evaluate the performance of the proposed method as well as the impact of ignoring measurement error in covariates. The proposed method is applied to analyse a data set arising from the Busselton Health study.