Non-parametric Maximum Likelihood Estimation for Cox Regression with Subject-Specific Measurement Error

Many epidemiological studies have been conducted to identify an association between nutrient consumption and chronic disease risk. To this problem, Cox regression with additive covariate measurement error has been well developed in the literature. However, researchers are concerned with the validity of the additive measurement error assumption for self-report nutrient data. Recently, some study designs using more reliable biomarker data have been considered, in which the additive measurement error assumption is more likely to hold. Biomarker data are often available in a subcohort. Self-report data often encounter with a variety of serious biases. Complications arise primarily because the magnitude of measurement errors is often associated with some characteristics of a study subject. A more general measurement error model has been developed for self-report data. In this paper, a non-parametric maximum likelihood (NPML) estimator using an EM algorithm is proposed to simultaneously adjust for the general measurement errors. Copyright (c) Board of the Foundation of the Scandinavian Journal of Statistics 2008.