Statistical analysis for categorical data subject to misclassification errors

对存在错误分类错误的分类数据进行统计分析

• 批准号: RGPIN-2021-03535
• 负责人: Liu, Juxin
• 金额: $ 1.53万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742604
• 关键词: Statistical; analysis; categorical; data; subject

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.

该提案旨在解决分析未得到完美评估的分类数据时出现的一些问题。其主要目标是（1）丰富分类数据的统计方法工具箱，（2）为应用实践者提供有效和有见地的指导方针。在健康科学、心理学和遗传学等许多应用领域中，经常会遇到分类错误的问题。例如，吸烟状况通过自我报告问卷来衡量。由于害怕被污名化，参与者可能并不总是报告他们真正的吸烟行为。人们早就认识到，简单地忽略这些错误可能会导致有偏见的推断，可能会严重扭曲真相。大多数现有文献只关注错误分类的协变量（通常是二进制）或仅错误分类的响应变量（通常是二进制）。在真实的世界中，响应变量和协变量的同时误分类问题越来越普遍，但对于这一问题的研究却相当有限。例如，共享相同信息来源的研究（例如，自我报告问卷）以评估响应变量（例如疾病状态）和协变量（例如，吸烟状况）往往具有相关的误分类错误。该提案旨在从三个不同但相互关联的方向解决真实的应用所带来的挑战：（1）开发能够适应响应变量和协变量的相关误分类的模型;（2）评估使用不正确的误分类模型的后果;（3）开发用于诊断关于误分类结构的假设的有效性的测试程序。