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

项目概要/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 用于艾滋病研究。然而，这些数据源通常在跨 多个变量，这可能导致有偏见的研究结果和误导性的结论。此外，EHR数据 来源往往缺乏金标准的测量，需要明确界定的存在或不存在的 共病（例如，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 收集他们病历子样本的额外数据。通过结合丰富的，但容易出错的EHR 所有研究受试者的数据以及从受试者子样本中收集的金标准/验证数据， 研究人员可以改进研究估计。具体来说，研究人员可以消除估计的偏差， 他们只使用EHR数据，他们可以提高精度（例如，较窄的置信区间） 如果他们只使用具有黄金标准/验证数据的子样本，估计数将减少。在早期的研究中，我们 开发了统计方法和软件，将联合收割机EHR数据与经过验证的子样本数据相结合。我们 开发了最佳的多波设计，用于数据验证的目标记录。重要的是，我们应用了这些 使用国际流行病学回顾性观察数据的多项HIV研究方法 艾滋病评估数据库。然而，在我们的应用程序中，我们遇到了额外的 尚未解决的挑战。特别是，将昂贵的， 前瞻性收集的、稀疏测量的黄金标准数据（例如，每年一次） 患者的EHR数据，收集更频繁的大量患者。我们将 开发处理这种情况的方法，我们将开发统计设计，以更好地选择 参与者应接触前瞻性数据收集和患者记录应 验证.我们还将开发统计方法来解决使用EHR遇到的其他挑战 数据，包括当纳入研究时容易出错时如何将验证数据纳入研究，以及 用于处理更复杂类型的数据的方法（例如，区间删失数据），其中缺乏 处理易出错数据的技术。我们的方法和设计将集中在多个 插补、最大似然和广义搜索技术。开源工具和教程将 帮助研究人员实施这些新方法和研究设计。的方法和 设计将应用于来自IeDEA网络的数据，以估计肝脏疾病的发病率和风险因素。 在东非和拉丁美洲，艾滋病病毒感染者的纤维化/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.