Yan Ying, Yi Grace Y
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada.
Lifetime Data Anal. 2016 Jul;22(3):321-42. doi: 10.1007/s10985-015-9340-1. Epub 2015 Sep 2.
Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.
协变量测量误差在生存分析中普遍存在。在比例风险模型下，测量误差的影响已得到充分研究，并且已经开发了各种推断方法来校正该模型下的误差影响。相比之下，加法风险模型下受误差污染的生存数据受到的关注相对较少。在本文中，我们通过探索测量误差对参数估计和风险函数变化的影响来研究这个问题。与Cox比例风险模型的充分记录结果相反，揭示了测量误差影响的新见解。我们提出了一类偏差校正估计量，其中包括某些现有估计量作为特殊情况。此外，我们利用回归校准方法来减少测量误差的影响。建立了所开发方法的理论结果，并进行了数值评估以说明我们方法的有限样本性能。
