Expected estimating equations via EM for proportional hazards regression with covariate misclassification

In epidemiological and medical studies, covariate misclassification may occur when the observed categorical variables are not perfect measurements for an unobserved categorical latent predictor. It is well known that covariate measurement error in Cox regression may lead to biased estimation. Misclassification in covariates will cause bias, and adjustment for misclassification will be challenging when the gold standard variables are not available. In general, statistical modeling for misclassification is very different from that of the measurement error. In this paper, we investigate an approximate induced hazard estimator and propose an expected estimating equation estimator via an expectation–maximization algorithm to accommodate covariate misclassification when multiple surrogate variables are available. Finite sample performance is examined via simulation studies. The proposed method and other methods are applied to a human immunodeficiency virus clinical trial in which a few behavior variables from questionnaires are used as surrogates for a latent behavior variable.