Statistical analysis for categorical data subject to misclassification errors

该提案旨在解决分析分类数据时出现的一些新问题，这些数据没有得到完美的评估。其主要目标是(1)丰富关于分类错误的分类数据的统计方法工具箱，(2)为应用从业者提供有效和有见地的指导方针。易受分类错误影响的分类数据在健康科学、心理学和遗传学研究等许多应用领域中经常遇到。例如，吸烟状况是通过自我报告问卷来衡量的。由于害怕被污名化，参与者可能并不总是报告他们的真实吸烟行为。长期以来，人们已经认识到，简单地忽视这些错误可能会导致有偏见的推断，从而可能严重扭曲事实。现有的大多数文献要么只关注错误分类的协变量(通常是二进制的)，要么只关注错误分类的反应变量(通常是二进制的)。在处理响应变量和协变量的同时错误分类方面所做的工作相当有限，这在现实世界中已经变得越来越普遍。例如，共享相同信息来源(例如，自我报告问卷)以评估响应变量(例如，疾病状况)和协变量(例如，吸烟状况)的研究往往具有相关的错误分类错误。这一建议旨在解决实际应用在三个不同但相互关联的方向上产生的挑战：(1)开发能够容纳响应变量和协变量的相关错误分类的模型；(2)评估使用错误分类模型的后果；(3)开发测试程序以诊断关于错误分类结构的假设的有效性。

The proposal aims to tackle some emerging issues in analyzing categorical data that are not perfectly assessed. The primary goal is to (1) enrich the toolbox of statistical methodologies on categorical data subject to misclassification errors and (2) provide valid and insightful guidelines for applied practitioners. Categorical data subject to misclassification errors has been commonly encountered in many applied fields such as health science, psychology, and genetic studies. For instance, smoking status is measured by a self-report questionnaire. Due to the fear of stigmatization, participants may not always report their true smoking behaviour. It has been long recognized that simply ignoring such errors can lead to biased inference that may severely distort the truth. Most of the existing literature focus on either only misclassified covariates (usually binary) or only misclassified response variables (usually binary). Rather limited work has been done for dealing with simultaneous misclassification of response variables and covariates, which has been becoming increasingly common in real world. For example, studies that share the same sources of information (e.g., self-report questionnaire) to assess both the response variable (e.g. disease status) and the covariate (e.g., smoking status) tend to have correlated misclassification errors. This proposal aims to tackle the challenges arising from the real applications in three different yet interconnected directions: (1) developing models that can accommodate correlated misclassification of the response variable and the covariate; (2) assessing consequences of using incorrect misclassification models; (3) developing test procedures for diagnosing the validity of assumptions about the misclassification structure.