Accessible Handling of Misclassified or Missing Binary Variables in CER Studies

CER 研究中错误分类或缺失的二元变量的可访问处理

• 批准号: 8037394
• 负责人: Robert H Lyles
• 金额: $ 44.17万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-24 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8037394
• 关键词: Accident and Emergency department; Address; Bacterial Vaginosis; Biological Assay; Clinical; Cohort Studies; Communities; Computer software; Data; Data Analyses; Data Collection; Data Sources; Diagnosis; Diagnostic Procedure; Epidemiologic Studies; Epidemiology; Equation; Fostering; Fundus photography; Goals; HIV; Intention; Investigation; Literature; Logistic Regressions; Measures; Methods; Modeling; Motivation; Outcome; Participant; Process; Research; Research Design; Research Personnel; Resource Allocation; Sampling; Series; Statistical Methods; Techniques; Time; Validation; Visit; abstracting; analytical method; base; case control; combat; comparative effectiveness; cost; design; effectiveness research; interest; programs; research study; user-friendly

DESCRIPTION (provided by applicant): Common but often overlooked threats to the validity of comparative effectiveness research (CER) studies include the misclassification or missingness of binary variables that are crucial to the ultimate analysis of the data. These variables potentially include the outcome of interest in standard or repeated measures logistic regression models, the factor (exposure) of interest, or an important confounder of the association under study. This proposal seeks to facilitate the investigation of the resulting biases to which a given CER analysis may be subject, and to provide study design-based remedial measures via which validity can be restored. The focus is upon statistical methods for conducting sensitivity analyses, as well as methods designed to make efficient use of supplemental data sources. The latter include validation data (in the case of misclassification), and so-called reassessment data (in the case of potentially informative missingness). A primary consideration throughout includes the incorporation of subject-specific covariates into the model of interest, as well as into models for the underlying misclassification or missingness process. Another primary goal is to establish a relatively consistent likelihood-based framework for all proposed analyses incorporating supplemental data, and to provide user-friendly programs utilizing common statistical software in order to make the methods broadly and readily accessible to those conducting CER. While not limited to specific applications, the proposed research draws motivation from and lends itself to illustration via two real-world studies. The first is the HIV Epidemiology Research Study (HERS), an observational cohort study in which the binary diagnosis of bacterial vaginosis was made at repeated visits via both error-prone and sophisticated assay techniques. The second is an emergency department-based ophthalmologic study in which non-dilated ocular fundus photography will be used for diagnosing serious ocular conditions, and will be compared against existing standard diagnostic methods. Both studies involve internal validation data to facilitate corrections for misclassification based on a fallible diagnostic method, and both are also subject to missing outcome and/or predictor data. PUBLIC HEALTH RELEVANCE: The goal of this project is to provide statistical methods to aid comparative effectiveness research (CER) investigators with common problems encountered in data analysis. The problems upon which the project focuses come about when binary ("yes/no") data are subject to being incorrectly measured (misclassified), or when they are sometimes not observed (missing) for reasons that might relate to information about subjects in the study. The intention is to provide CER investigators with methods that are relatively easy to use, yet effective and powerful for combating these challenges to valid data analysis.

描述（由申请人提供）：比较有效性研究（CER）研究有效性的常见但经常被忽视的威胁包括对数据最终分析至关重要的二元变量的错误分类或缺失。这些变量可能包括标准或重复测量逻辑回归模型中感兴趣的结果、感兴趣的因素（暴露）或所研究关联的重要混杂因素。该提案旨在促进对给定 CER 分析可能产生的偏差的调查，并提供基于研究设计的补救措施，通过这些措施可以恢复有效性。重点是进行敏感性分析的统计方法，以及旨在有效利用补充数据源的方法。后者包括验证数据（在错误分类的情况下）和所谓的重新评估数据（在潜在信息缺失的情况下）。整个过程中的主要考虑因素包括将特定于主题的协变量纳入感兴趣的模型以及潜在的错误分类或缺失过程的模型中。另一个主要目标是为所有拟议的纳入补充数据的分析建立一个相对一致的基于可能性的框架，并利用通用统计软件提供用户友好的程序，以使进行 CER 的人员广泛且容易地使用这些方法。虽然不限于特定应用，但拟议的研究从两项现实世界的研究中汲取动力并有助于说明。第一个是艾滋病毒流行病学研究（HERS），这是一项观察性队列研究，通过重复访问，通过容易出错的复杂检测技术对细菌性阴道病进行二元诊断。第二项是基于急诊室的眼科研究，其中非散瞳眼底摄影将用于诊断严重的眼部疾病，并将与现有的标准诊断方法进行比较。这两项研究都涉及内部验证数据，以促进基于易错的诊断方法的错误分类的纠正，并且两项研究也都受到结果和/或预测数据缺失的影响。 公共卫生相关性：该项目的目标是提供统计方法，帮助比较有效性研究 (CER) 调查人员解决数据分析中遇到的常见问题。当二元（“是/否”）数据被错误测量（错误分类），或者由于可能与研究中受试者信息相关的原因有时未观察到（缺失）数据时，该项目关注的问题就会出现。目的是为 CER 研究人员提供相对易于使用、但有效且强大的方法来应对有效数据分析的这些挑战。