Approximate profile likelihood estimation for Cox regression with covariate measurement error

被引：2

作者：

Cao, Zhiqiang ^{[1
]}

Wong, Man Yu ^{[2
]}

机构：

[1] Shenzhen Technol Univ, Coll Big Data & Internet, Shenzhen, Peoples R China

[2] Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, Peoples R China

来源：

STATISTICS IN MEDICINE | 2022年 / 41卷 / 05期

关键词：

approximate profile likelihood; Cox regression; measurement error; nutritional epidemiology; PROPORTIONAL HAZARDS MODEL; FAILURE TIME REGRESSION; PLASMA VITAMIN-C; SURVIVAL-DATA; VEGETABLE CONSUMPTION; RISK; VARIABLES; CANCER; FRUIT; CALIBRATION;

D O I：

10.1002/sim.9324

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In nutritional epidemiology, measurement error in covariates is a well-known problem since dietary intakes are usually assessed through self-reporting. In this article, we consider an additive error model in which error variables are highly correlated, and propose a new method called approximate profile likelihood estimation (APLE) for covariates measured with error in the Cox regression. Asymptotic normality of this estimator is established under regularity conditions, and simulation studies are conducted to examine the finite sample performance of the proposed estimator empirically. Moreover, the popular correction method called regression calibration is shown to be a special case of APLE. We then apply APLE to deal with measurement error in some nutrients of interest in the EPIC-InterAct Study under a sensitivity analysis framework.

引用

页码：910 / 931

页数：22

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