Novel Missing Data Approaches for Corrupted Longitudinal Data

现代纵向数据库可能涉及组合多个数据集和不同的测量，可能不是完整和干净的。由于不完整，执行下游统计分析并不简单。这个项目旨在开发新的统计方法，以处理从缺失数据的角度来看的不完整性。该项目还将处理癌症研究的数据链接问题，研究人员已将他们的临床试验数据与医疗保险和医疗补助中心的数据库联系起来。正在开发的方法将应用于国家阿尔茨海默氏症协调中心数据库和西南肿瘤组织(SWOG)癌症研究网络的前列腺癌预防试验数据。这些方法还将被用来解决新冠肺炎疫情和其他破坏数据收集的传染病造成的数据收集问题。该项目为研究生提供培训，为本科生提供研究机会。该项目侧重于三个研究问题。首先，PI旨在开发一种逆概率加权(IPW)方法来处理一个纵向数据库与另一个数据库的链接。该IPW方法基于链接概率对观测进行重新加权，以解决链接问题。该项目将IPW方法应用于数据链接问题，并开发了一种新的效率理论。该项目的第二部分考虑了纵向数据库中的量度变化问题，即在收集纵向数据期间将量度更新为较新的版本。PI打算将其描述为丢失数据的问题，并引入一种结合潜变量和分位数回归的新方法，以创建新旧测量版本之间的转换。在项目的第三部分，PI计划开发一种“双”半参数估计器，用于处理响应和协变量中的缺失，并研究效率理论。PI将设计一套关于协变量缺失的识别假设，以确保可识别性。然后，可以从识别假设中推导出一种方法来归因于缺失的协变量，将情况转换为仅缺少响应的标准情况。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Modern longitudinal databases, which can involve combined multiple datasets and varying measurements, may not be complete and clean. Because of the incompleteness, performing downstream statistical analysis is not straightforward. This project aims to develop novel statistical methods to handle incompleteness from missing data perspectives. The project will also deal with the data linkage issue from cancer research, where researchers have linked their clinical trial data to the Centers for Medicare & Medicaid database. The methods under development will be applied to the National Alzheimer’s Coordinating Center database and Prostate Cancer Prevention Trial data in the Southwest Oncology Group (SWOG) Cancer Research Network. The methods will also be used to resolve data collection problems caused by the COVID-19 pandemic and other infectious diseases that corrupt data collection. The project offers training for graduate students and research opportunities for undergraduate students.The project focuses on three research questions. First, the PI aims to develop an inverse probability weighting (IPW) approach to handle linking of one longitudinal database with another database. This IPW method reweights observations based on the linking probability to account for the linking issue. The project will apply the IPW method to the data linkage issue and develop a new efficiency theory. The second part of the project considers the changing-measurement problem in a longitudinal database, in which a measurement is updated to a newer version during the collection of longitudinal data. The PI intends to formulate this as a missing data problem and introduce a new approach combining latent variable and quantile regression to create a conversion between the new and the old versions of the measurement. In the third part of the project, the PI plans to develop a "doubly" semi-parametric estimator for handling missingness in both responses and covariates and to study the efficiency theory. The PI will design a set of identifying assumptions on the missingness of covariates to ensure identifiability. A method can then be derived from the identifying assumptions to impute the missing covariates, converting the situation to a standard one in which responses alone are missing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.