许多研究领域（例如营养流行病学）在建模时可能会遇到连续协变量的测量误差和分类变量的分类错误。众所周知，忽视测量误差或分类错误可能导致偏颇的结果。但是大多数研究都集中在分别解决这两个问题。在单个分析中同时解决测量误差和分类错误问题则研究较少。本文提出了一种新的校正方法，用于处理涉及多元连续协变量和分类变量的相关误差变量和分类错误。该方法不需要大量计算，因为已经得出了基于观测协变量的近似似然函数的封闭形式。在正则条件下建立了此提议的估计程序的渐近正态性，并通过模拟研究对其有限样本性能进行了测试。我们将这种新的估计方法应用于处理感兴趣的某些营养素的测量误差和欧洲前瞻性研究中关于癌症和营养干预互动研究数据中称为“身体活动”的分类变量的分类错误。分析结果显示，对于一组进行积极身体活动的女性，水果与2型糖尿病之间存在负相关，而对于不那么积极身体活动的人群，蛋白质与2型糖尿病之间存在正相关。实际身体活动对于降低2型糖尿病的风险比观察到的身体活动有更大的影响。

Many areas of research, such as nutritional epidemiology, may encounter measurement errors of continuous covariates and misclassification of categorical variables when modeling. It is well known that ignoring measurement errors or misclassification can lead to biased results. But most research has focused on solving these two problems separately. Addressing both measurement error and misclassification simultaneously in a single analysis is less actively studied. In this article, we propose a new correction method for a logistic regression to handle correlated error variables involved in multivariate continuous covariates and misclassification in a categorical variable simultaneously. It is not computationally intensive since a closed-form of the approximate likelihood function conditional on observed covariates is derived. The asymptotic normality of this proposed estimator is established under regularity conditions and its finite-sample performance is empirically examined by simulation studies. We apply this new estimation method to handle measurement error in some nutrients of interest and misclassification of a categorical variable named physical activity in the European Prospective Investigation into Cancer and Nutrition-InterAct Study data. Analyses show that fruit is negatively associated with type 2 diabetes for a group of women doing active physical activity, protein has positive association with type 2 diabetes for the group of less active physical activity, and actual physical activity has a greater effect on reducing the risk of type 2 diabetes than observed physical activity.