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缺失。这些变量可能包括标准或重复测量逻辑回归模型的结果，感兴趣的因素（暴露），或研究中关联的重要混杂因素。本提案旨在促进对特定CER分析可能受到的结果偏差的调查，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）。贯穿始终的主要考虑包括将特定于主题的协变量合并到感兴趣的模型中，以及合并到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纳入补充数据的拟议分析建立一个相对一致的基于可能性的框架，并提供使用通用统计软件的用户友好程序，以便使进行CER的人员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现实世界的研究中获得了动力，并使其成为例证。第一个是HIV流行病学研究（HERS），这是一项观察性队列研究，通过易出错和复杂的检测技术在反复就诊时进行细菌性阴道病的二元诊断。第二项是一项以急诊科为基础的眼科研究，该研究将使用非扩张眼底摄影技术诊断严重眼部疾病，并将其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，以促进基于易出错的诊断方法的错误分类的纠正，而且这两项研究也都受到缺少结果和/或预测数据的影响。

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.