Approximate profile likelihood estimation for Cox regression with covariate measurement error

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.

Automated summary

In this article, a new method called approximate profile likelihood estimation (APLE) is proposed to handle measurement error in covariates in Cox regression. The asymptotic normality of APLE is established under regularity conditions, and simulation studies are conducted to empirically examine its finite sample performance. Additionally, the widely used correction method called regression calibration is shown to be a special case of APLE. APLE is then applied to address measurement error in nutrients of interest in the EPIC-InterAct Study within a sensitivity analysis framework.