A Modified Partial Likelihood Score Method for Cox Regression with Covariate Error Under the Internal Validation Design

• Activity Number: 27 - SPEED: Causal Inference and Related Methodology Part 1
• Type: Contributed
• Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Xin Zhou* and David Zucker and Xiaomei Liao and Yi Li and Donna Spiegelman
• Companies: Yale School of Public Health and The Hebrew University of Jerusalem and AbbVie and University of Michigan School of Public Health and Yale School of Public Health
• Keywords: Cox model; Measurement error; Modified score

Abstract

In many applications, however, the covariate is not measured exactly, but is subject to measurement error of some degree, often substantial. Thus, instead of observing the true covariate, we observe a surrogate measure. In the internal validation design, there is a subsample of individuals with a measurement on both the true covariate and the surrogate. We develop a new method for covariate error correction in the Cox survival regression model, given a modest sample of internal validation data. Unlike most previous methods for this setting, our method can handle covariate error of arbitrary form. Asymptotic properties of the estimator are derived. In a simulation study, the method was found to perform very well in terms of bias reduction and confidence interval coverage. The method is applied to data from Health Professionals Follow-Up Study on the effect of diet on incidence of Type II diabetes.