Accessible Handling of Misclassified or Missing Binary Variables in CER Studies

描述(由申请人提供)：对比较有效性研究(CER)有效性的常见但经常被忽视的威胁包括对数据的最终分析至关重要的二元变量的错误分类或遗漏。这些变量可能包括在标准或重复测量Logistic回归模型中感兴趣的结果，感兴趣的因素(暴露)，或研究中关联的重要混杂因素。这项建议旨在促进对某一特定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，以及为有效利用补充数据来源而设计的方法。后者包括确认数据(在分类错误的情况下)和所谓的重新评估数据(在可能缺少信息的情况下)。贯穿始终的一个主要考虑包括将特定于主题的协变量纳入感兴趣的模型，以及纳入潜在的错误分类或遗漏过程的模型。另一个主要目标是为纳入补充数据的所有拟议分析建立一个相对一致的基于似然的框架，并利用通用统计软件提供用户友好的方案，以使进行CER的人能够广泛和容易地使用这些方法。虽然不局限于具体的应用，但拟议的研究从两个真实世界的研究中获得了动力，并通过两个真实世界的研究进行了说明。第一项是艾滋病毒流行病学研究(HERS)，这是一项观察性队列研究，其中细菌性阴道病的二元诊断是通过容易出错的和复杂的化验技术在反复就诊时做出的。第二项是以急诊科为基础的眼科研究，其中非扩张性眼底照相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，以便于根据容易出错的诊断方法纠正错误分类，而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，以帮助比较有效性研究(CER)研究人员解决在数据分析中遇到的常见问题。当二进制数据(“是/否”)可能被错误地测量(错误分类)，或者有时由于可能与研究对象的信息有关的原因而没有被观察到(丢失)时，该项目所关注的问题就会出现。其目的是为CER调查人员提供相对容易使用、但有效和强大的方法，以应对有效数据分析的这些挑战。

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.