A functional method for the conditional logistic regression with errors-in-covariates
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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. © 2012 Copyright American Statistical Association and Taylor & Francis.
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