Fusion Pursuit for Pattern-Mixture Models with Application to Longitudinal Studies with Nonignorable Missing Data

模式混合模型的融合追踪及其在不可忽略缺失数据纵向研究中的应用

• 批准号: 2310217
• 负责人: Lu Tang
• 金额: $ 20万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310217&HistoricalAwards=false
• 关键词: Fusion; Pursuit; Pattern; Mixture; Models

Missing data is ubiquitous in scientific research, challenging the accuracy of statistical analyses, the results of which will ultimately generate knowledge and guide policy or decision making. This project aims to develop a suite of new statistical tools to address the challenges in analyzing longitudinal studies with nonignorable missingness, such as informative dropout. The principal investigator will incorporate a machine learning approach termed fusion pursuit into the pattern-mixture modeling framework to achieve more efficient estimation and inference in longitudinal association analyses. The methods will find broad use in survey, medical, and policy research, and in other areas that involve longitudinal studies with a heavy presence of missing data. The project will also integrate research with the training of graduate students, developing trainees in the topics proposed through research involvement and teaching.The project will extend the estimation and inference capabilities of pattern-mixture models in analyzing longitudinal data that are subject to missing not at random. Formulated in the framework of generalized estimating equations, this research develops a post-stratification fusion pursuit strategy to overcome over-stratification by missing-data patterns, which is the bottleneck of pattern-mixture models in analyzing large-scale data sets. The project will first develop a regularization approach to simultaneously collapse redundant missing-data pattern strata and estimate parameters of interest. To ensure valid statistical inference, the project will then develop a post-fusion inference approach to derive valid and generalizable confidence regions. Finally, the project will demonstrate the developed approaches in three situations of real-world longitudinal studies, including missing visits, missing covariates, and distributed data. The research project is expected to broaden the use cases of pattern-mixture models in analyzing longitudinal studies with missing data.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.

缺少数据在科学研究中无处不在，挑战了统计分析的准确性，结果最终将产生知识和指导政策或决策。 该项目旨在开发一套新的统计工具，以解决分析纵向研究的挑战，这些纵向研究不可忽视，例如辍学的辍学。首席研究者将将称为融合追求的机器学习方法纳入模式建模框架中，以实现纵向关联分析中更有效的估计和推理。这些方法将在调查，医学和政策研究中以及在涉及纵向研究的其他领域中找到广泛的用途，这些研究大量存在数据。该项目还将将研究与研究生的培训相结合，开发通过研究参与和教学提出的主题的受训者。该项目将扩展模拟模型模型的估计和推理能力，以分析纵向数据，这些纵向数据可能不会随机丢失。本研究是在广义估计方程式的框架中制成的，开发了一种分层后的追求策略，以克服缺失数据模式的过度分层，这是分析大型数据集的模式混合模型的瓶颈。该项目将首先开发一种正规化方法，以同时崩溃冗余的缺失数据模式层和估计感兴趣的参数。为了确保有效的统计推断，该项目将开发一种融合后推断方法，以获得有效且可推广的置信区域。最后，该项目将在现实世界纵向研究的三种情况下展示开发的方法，包括缺少访问，缺失协变量和分布式数据。预计该研究项目将在分析缺少数据的纵向研究中扩大模式模型的用例。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。