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

摘要(Shepherd and Shaw，R01，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) 人们越来越有兴趣定期使用管理电子健康记录(EHR)数据和其他 收集数据来源，作为支持艾滋病毒/艾滋病研究的具有成本效益的手段。观测性的验证 队列和电子健康记录数据表明，在这些类型的数据中存在大量错误。可能会有 失败和审查时间(例如，从ART开始到临床事件的时间)、事件分类和 协变量(例如，抗逆转录病毒治疗开始时的CD4)，在这些变量中的误差大小之间具有很强的相关性 变量。这些相关误差可能会使估计产生偏差。理想情况下，研究人员可以验证他们的 数据并使用从该子样本中学习的信息来改进对整个队列的估计，从而 在不验证整个数据库的情况下获得有效的估计。然而，目前缺乏可用的方法 和软件来纠正这些类型的错误，以获得事件的时间结果，这是执行的主要障碍 对这些类型的数据做出正确的推断。对于要验证哪些记录和变量，也几乎没有指导 优化资源配置。该项目将创建新的统计方法，以减少或消除估计 由故障时间结果和相关协变量中的相关误差引起的偏差。已开发的方法 将使用通过数据验证或审计子集获得的有关测量误差结构的信息，以 调整估计并纠正未验证数据中保留的错误。该项目将发展和 检查回归校准、校正分数和多重补偿方法的扩展，增强 利用倾斜技术来解决这些相关的错误。该项目还将开发高效的数据验证 以及审计抽样设计，该抽样设计使用自适应、多波抽样，以针对连续验证和 对信息丰富的患者亚组进行子集审计。将开发开放源码工具以允许 研究人员实施这些方法和研究设计。这些方法和设计将应用于数据 从国际流行病学数据库评估艾滋病(IeDEA)估计发病率 结核病和卡波西肉瘤及其结局、危险因素相关性和时间趋势 东非和拉丁美洲的艾滋病毒携带者。

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.