Weibull Regression With Both Measurement Error and Misclassification in Covariates

The problem of measurement error and misclassification in covariates is ubiquitous in nutritional epidemiology and some other research areas, which often leads to biased estimate and loss of power. However, addressing both measurement error and misclassification simultaneously in a single analysis is challenged and less actively studied, especially in regression model for survival data with censoring. The approximate maximum likelihood estimation (AMLE) has been proved to be an effective method to correct both measurement error and misclassification simultaneously in a logistic regression model. However, its impact on survival analysis models has not been studied. In this paper, we study biases caused by both measurement error and misclassification in covariates from a Weibull accelerated failure time model, and explore the use of AMLE and its asymptotic properties to correct these biases. Extensive simulation studies are conducted to evaluate the finite-sample performance of the resulting estimator. The proposed method is then applied to deal with measurement error and misclassification in some nutrients of interest from the EPIC-InterAct Study.