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Novel Missing Data Approaches for Corrupted Longitudinal Data

针对损坏的纵向数据的新颖的缺失数据方法

基本信息

批准号：
2112907
负责人：
Yen-Chi Chen
金额：
$ 14.73万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112907&HistoricalAwards=false
关键词：
Novel Missing Data Approaches Corrupted

项目摘要

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.

现代纵向数据库可能涉及组合的多个数据集和不同的测量，可能不完整和干净。由于不完整性，进行下游统计分析并不简单。该项目旨在开发新的统计方法来处理缺失数据视角的不完整性。该项目还将处理来自癌症研究的数据链接问题，研究人员将他们的临床试验数据与医疗保险医疗补助中心数据库相关联。正在开发的方法将应用于国家阿尔茨海默病协调中心数据库和西南肿瘤学组（SWOG）癌症研究网络的前列腺癌预防试验数据。这些方法还将用于解决COVID-19大流行和其他破坏数据收集的传染病引起的数据收集问题。该项目为研究生提供培训，为本科生提供研究机会。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值重新加权，以解决链接问题。该项目将把IPW方法应用于数据连接问题，并开发一种新的效率理论。该项目的第二部分考虑了纵向数据库中的变化测量问题，在纵向数据收集过程中，测量被更新为更新的版本。PI打算将其表述为缺失数据问题，并引入一种结合潜在变量和分位数回归的新方法，以创建新旧版本测量值之间的转换。在项目的第三部分，PI计划开发一个“双”半参数估计，用于处理响应和协变量中的缺失，并研究效率理论。PI将设计一组关于协变量缺失的识别假设，以确保可识别性。一种方法可以从识别的假设来估算缺失的协变量，将情况转换为标准的情况下，只有响应是missed.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。