Statistical methods and designs for correlated outcome and covariate errors in studies of HIV/AIDS

项目摘要/ASBTRACT 电子健康记录(EHR)和其他常规收集的数据通常被用作具有成本效益的数据源 用于艾滋病毒/艾滋病研究。然而，众所周知，这些数据源容易出错，通常是跨 多变量，这可能导致研究结果的偏差和误导性的结论。此外，EHR数据 来源通常缺乏明确定义存在或不存在的黄金标准衡量标准 合并症(如肝纤维化)。为了解决EHR数据源的局限性，研究人员可以验证或 收集关于他们的患者记录的子样本的额外数据。通过将丰富但容易出错的EHR 关于所有研究对象的数据，以及在受试者的子样本上收集的黄金标准/验证数据， 研究人员可以改进研究估计。具体地说，研究人员可以消除估计HAD的偏差 他们只使用了EHR数据，他们可以提高研究的精确度(例如，更窄的可信区间 估计他们只使用带有黄金标准/验证数据的子样本。在早期的研究中，我们 开发了统计方法和软件，将电子健康记录数据与经过验证的数据子样本相结合。我们 开发了针对数据验证的记录的最佳、多波设计。重要的是，我们应用了这些 利用国际流行病学的回溯性观察数据进行多项HIV研究的方法 评估艾滋病的数据库(IeDEA)。然而，在我们的应用程序中，我们遇到了其他 尚未解决的挑战。特别是，将昂贵的、 对子样本进行稀疏测量(例如，每年一次)的前瞻性收集的金本位数据 更频繁地收集大量患者的EHR数据的患者。我们会 开发处理这种设置的方法，我们将开发统计设计以更好地选择 应与参与者接洽以进行预期的数据收集，以及应提供哪些患者记录 已验证。我们还将开发统计方法来解决在使用电子病历时遇到的其他挑战 数据，包括当纳入研究容易出错时如何将验证数据纳入研究，以及 处理更复杂类型的数据(例如，间隔审查数据)的方法，而这些数据缺乏 处理容易出错的数据的技术。我们的方法和设计将专注于扩展多个 归因法、最大似然法和广义RAKING技术。开源工具和教程将包括 旨在帮助研究人员实施这些新方法和研究设计。方法和方法 设计将应用于来自IeDEA网络的数据，以估计肝脏的发病率和风险因素 东非和拉丁美洲艾滋病毒携带者的纤维化/脂肪变性和虚弱。

PROJECT SUMMARY/ASBTRACT Electronic health record (EHR) and other routinely collected data are often used as cost-effective data sources for HIV/AIDS research. These data sources, however, are known to be prone to errors, typically across multiple variables, which can lead to biased study results and misleading conclusions. In addition, EHR data sources often lack gold-standard measurements that are needed to clearly define the presence or absence of co-morbidities (e.g., liver fibrosis). To address limitations of EHR data sources, researchers can validate or collect additional data on a subsample of their patient records. By combining the rich, but error-prone EHR data on all study subjects with the gold-standard / validated data collected on a subsample of subjects, researchers can improve study estimates. Specifically, researchers can eliminate the bias of estimates had they only used the EHR data, and they can improve the precision (e.g., narrower confidence intervals) of study estimates had they only used the subsample with gold-standard / validated data. In earlier research, we developed statistical methods and software to combine EHR data with validated sub-samples of data. We developed optimal, multi-wave designs for targeting records for data validation. Importantly, we applied these methods to multiple HIV studies using retrospective observational data from the International epidemiology Databases to Evaluate AIDS (IeDEA). However, in our applications, we have encountered additional challenges that have not yet been addressed. In particular, there is great potential in combining expensive, prospectively collected, gold-standard data that are sparsely measured (e.g., once per year) on a sub-sample of patients with EHR data that are collected much more frequently on a larger number of patients. We will develop methods to handle this setting, and we will develop statistical designs to better select which participants should be approached for prospective data collection and which patient records should be validated. We will also develop statistical methods to address other challenges encountered with using EHR data, including how to incorporate validation data into studies when inclusion in the study is error-prone, and methods to address more complex types of data (e.g., interval censored data), for which there are a lack of techniques to handle error-prone data. Our methods and designs will focus on extensions of multiple imputation, maximum likelihood, and generalized raking techniques. Open source tools and tutorials will be developed to help researchers to implement these novel methods and study designs. The methods and designs will be applied to data from the IeDEA network to estimate the incidence of and risk factors for liver fibrosis/steatosis and frailty among people living with HIV in East Africa and Latin America.