A functional method for the conditional logistic regression with errors-in-covariates

Abstract

In this article, we develop a functional approach for handling errors-in-covariates in matched case-control studies which are commonly analysed through the conditional logistic regression. We propose to estimate the parameters from a set of unbiased estimating equations. We require that the moment-generating function of the measurement errors exists. We investigate the asymptotic properties of the estimators. The finite sample performance of the method is judged via simulation studies. The proposed methodology is illustrated by analysing the data from the NIH-AARP Diet and Health study.

The Extensively Corrected Score for Measurement Error Models

“…The weighing and correcting schemes (steps 2 and 3) for constructing estimating functions have been adopted in some measurement error literatures (e.g. Huang & Wang (), Yi & Lawless (), Yi et al () and Sinha ()). However, because of the lack of duplicating or reweighing schemes (steps 1 and 4), their estimating functions are less efficient and does not reduce to original scores when there is no measurement error.…”