Case-control studies are important and useful methods for studying health outcomes and many methods have been developed for analyzing case-control data. Those methods, however, are vulnerable to mismeasurement of variables; biased results are often produced if such a feature is ignored. In this paper, we develop an inference method for handling case-control data with interacting misclassified covariates. We use the prospective logistic regression model to feature the development of the disease. To characterize the misclassification process, we consider a practical situation where replicated measurements of error-prone covariates are available. Our work is motivated in part by a breast cancer case-control study where two binary covariates are subject to misclassification. Extensions to other settings are outlined.

中文翻译：

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具有相互作用的错误分类协变量的病例对照数据分析

病例对照研究是研究健康结果的重要和有用的方法，并且已经开发出许多方法来分析病例对照数据。但是，这些方法很容易错误地测量变量。如果忽略此功能，通常会产生有偏差的结果。在本文中，我们开发了一种推理方法，该方法用于处理带有交互错误分类的协变量的案例控制数据。我们使用前瞻性逻辑回归模型来表征疾病的发展。为了描述错误分类过程的特征，我们考虑了一种实际情况，在该情况下，可以对易出错的协变量进行重复测量。我们的工作部分受乳腺癌病例对照研究的启发，该研究中两个二元协变量容易分类错误。概述了其他设置的扩展。