摘要：This paper deals with a regression model with covariates subject to misclassification. Simply ignoring the misclassification generally yields bias in parameter estimation. A corrected score function (Nakamura, 1990) is developed to obtain asymptotically unbiased estimates adjusting for the misclassification. It is proved that a corrected score function always exists for a misclassification model whose likelihood consists of independent contributions. This existence theorem for misclassification model is conspicuous since corrected score functions do not always exist for measurement error models with continuous covariates. For instance, a corrected score function does not exist for a logistic model with continuous covariates subject to normal random error (Stefanski, 1989). For illustration, the correction method is applied to generalized linear models and a simulation study based on the logistic regression model is performed. The misclassification are assumed to be nondifferential, which is normally assumed with prospective studies.
关键词：misclassification;measurement error;generalized linear model;corrected likelihood;corrected score
