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）：对比较有效性研究（CER）研究有效性的常见但经常被忽视的威胁包括二进制变量的错误分类或缺失，这对于数据的最终分析至关重要。这些变量可能包括在标准或重复测量的逻辑回归模型，感兴趣的因素（暴露）或研究关联的重要混杂因素中的兴趣结果。该提案旨在促进对特定CER分析可能受试者的偏见的调查，并提供基于研究设计的补救措施，可以通过这些补救措施来恢复有效性。重点是用于进行灵敏度分析的统计方法，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（对于潜在的信息丢失）。整个过程中的主要考虑因素包括将主题特定的协变量纳入感兴趣的模型，以及用于基础错误分类或丢失过程的模型中。另一个主要目标是为所有提议的分析建立一个相对一致的基于似然的框架，并使用常见的统计软件来提供对用户友好的程序，以使这些方法可以广泛且易于访问CER的人。虽然不限于特定应用，但拟议的研究却通过两项现实世界的研究吸引了动力，并将自己借给了插图。第一个是HIV流行病学研究（HERS），这是一项观察性队列研究，其中通过易用错误和复杂的测定技术在反复访问时对细菌性阴道病的二元诊断进行。第二个是一项基于急诊室的眼科研究，其中将使用未填充的眼底摄影来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。两项研究均涉及内部验证数据，以促进基于易犯错的诊断方法进行错误分类的校正，并且两者也遭受缺失的结果和/或预测数据。 公共卫生相关性：该项目的目的是提供统计方法，以帮助比较有效性研究（CER）研究人员在数据分析中遇到了常见问题。当二进制（“是/否”）数据被错误测量（错误分类）或有时未观察到（缺少）时，由于可能与研究中有关受试者的信息有关的原因，二进制（“是/否”）数据的问题出现的问题。目的是为CER研究人员提供相对易于使用的方法，但有效且有效，可以抵抗这些挑战进行有效的数据分析。