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

摘要（Shepherd和Shaw，R 01，研究中相关结果和协变量误差的统计方法 （艾滋病毒/艾滋病） 越来越多的人对使用行政电子健康记录（EHR）数据和其他常规数据感兴趣。 收集的数据源作为支持艾滋病毒/艾滋病研究的具有成本效益的手段。验证观察结果 队列和EHR数据表明这些类型的数据中存在大量错误。可能存在 故障和审查时间中的误差（例如，从ART开始至临床事件的时间）、事件分类，以及 协变量（例如，在ART开始时的CD 4），这些错误的幅度之间存在强相关性。 变量这些相关的误差会使估计产生偏差。理想情况下，研究人员可以验证他们的子样本， 数据并使用从该子样本中了解到的信息来改进整个队列的估计，从而 获得有效的估计，而无需验证整个数据库。然而，目前缺乏可用的方法， 和软件，以纠正这些类型的错误的时间到事件的结果是主要的障碍， 对这类数据的正确推断。对于哪些记录和变量需要验证，也没有什么指导 优化资源。该项目将创建新的统计方法，以减少或消除 失效时间结果和相关协变量中的相关误差引起的偏倚。开发的方法 将使用通过数据验证或审计子集获得的测量误差结构信息， 调整估计并纠正未验证数据中的错误。该项目将开发和 检查回归校准，校正分数和多重插补方法的扩展，增强 用耙技术来解决这些相关的错误。该项目还将开发有效的数据验证 和审计抽样设计，使用自适应，多波抽样，以目标连续验证， 对患者的信息亚组进行审计子集。将开发开源工具， 研究人员实施这些方法和研究设计。方法和设计将应用于数据 国际流行病学数据库评估艾滋病（IeDEA），以估计 结核病和卡波西肉瘤及其结果，危险因素关联，以及 东非和拉丁美洲的艾滋病毒感染者。

Abstract (Shepherd and Shaw, R01, Statistical methods for correlated outcome and covariate errors in studies of HIV/AIDS) There is growing interest in using administrative electronic health record (EHR) data and other routinely collected data sources as cost-effective means to support HIV/AIDS research. Validation of observational cohort and EHR data demonstrate the substantial presence of errors in these types of data. There may be errors in failure and censoring times (e.g., time from ART initiation to clinical events), event classifications, and covariates (e.g., CD4 at ART initiation), with strong correlation between the magnitudes of errors in these variables. These correlated errors can bias estimation. Ideally, researchers could validate a subsample of their data and use information learned from this subsample to improve estimation for the entire cohort, thereby obtaining valid estimates without validating the entire database. However, the current lack of available methods and software to correct for these types of errors for time-to-event outcomes are major barriers to performing correct inference on these types of data. There is also little guidance on what records and variables to validate to optimize resources. This project will create novel statistical methods for estimation to reduce or eliminate bias caused by correlated errors in failure-time outcomes and associated covariates. The developed methods will use information on the structure of the measurement error, gained by data validation or audit subsets, to adjust estimation and correct for errors that remain in the unvalidated data. The project will develop and examine extensions of regression calibration, corrected scores, and multiple imputation methods, augmented with raking techniques to address these correlated errors. The project will also develop efficient data validation and audit sampling designs that use adaptive, multi-wave sampling in order to target successive validation and audit subsets towards informative subgroups of patients. Open source tools will be developed to allow researchers to implement these methods and study designs. The methods and designs will be applied to data from the International Epidemiologic Databases to Evaluate AIDS (IeDEA) to estimate the incidence of tuberculosis and Kaposi's sarcoma and their outcomes, risk factor associations, and temporal trends among persons living with HIV in East Africa and Latin America.