Expected estimating equation using calibration data for generalized linear models with a mixture of Berkson and classical errors in covariates

Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.