Misclassifications in binary responses have long been a common problem in medical and health surveys. One way to handle misclassifications in clustered or longitudinal data is to incorporate the misclassification model through the generalized estimating equation (GEE) approach. However, existing methods are developed under a non‐survey setting and cannot be used directly for complex survey data. We propose a pseudo‐GEE method for the analysis of binary survey responses with misclassifications. We focus on cluster sampling and develop analysis strategies for analyzing binary survey responses with different forms of additional information for the misclassification process. The proposed methodology has several attractive features, including simultaneous inferences for both the response model and the association parameters. Finite sample performance of the proposed estimators is evaluated through simulation studies and an application using a real dataset from the Canadian Longitudinal Study on Aging.

中文翻译：

---

复杂调查中相关和错误分类的二元观测

长期以来，二进制响应中的错误分类一直是医学和健康调查中的常见问题。处理聚类或纵向数据中错误分类的一种方法是通过广义估计方程（GEE）方法合并错误分类模型。但是，现有方法是在非调查设置下开发的，不能直接用于复杂的调查数据。我们提出了一种伪GEE方法，用于对分类错误的二元调​​查响应进行分析。我们专注于整群抽样并开发分析策略，以针对误分类过程使用不同形式的附加信息来分析二元调查响应。所提出的方法具有几个吸引人的特征，包括对响应模型和关联参数的同时推断。