Semiparametric Techniques for Data Exploitation across Heterogeneous Populations

在从临床医学到政策研究的各个领域，研究人员经常可以获得来自多个相关但不同的人群的数据和信息。例如，在使用电子健康记录的生物医学研究中，由于各种资源限制导致样本量小，仅依靠标记数据进行分析可能效率低下。在临床试验环境中，医生可能需要解释随机对照试验的证据，该试验由人口统计学和其他历史特征与他们自己的患者截然不同的患者组成。同样，研究流感季节期间肺炎爆发的研究人员可能会发现，在非流感季节开发的预测模型是相关的和有用的。在所有这些情况下，至关重要的是开发能够适当地将来自一个群体的信息纳入另一个群体的统计分析的方法。该项目将开发一套统计可靠的方法，可以有效地将外部数据整合到初步研究中。该研究产品有可能应用于阿尔茨海默病、精神健康障碍、癌症、疼痛研究等各个领域。该项目还包括学科和跨学科层面的积极指导计划，惠及当地高中生、本科生、硕士生和博士生以及生物医学研究人员。在这个项目中，将利用半参数统计、稳健统计方法、统计学习技术、缺失数据分析和高维数据分析的独特组合来开发一套统计上合理的方法，将外部数据纳入初级研究。新方法具有最小的模型假设：它们要么是在假设精益框架下开发的，要么允许在过程中错误指定多个讨厌的模型。与不纳入外部数据的原始方法相比，新方法保证提高估计效率，提高统计能力，增强科学发现。此外，当正确指定干扰模型时，它们可以获得最大的效率增益。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In various fields, from clinical medicine to policy research, researchers often have access to data and information from multiple populations that are relevant but different. For example, in biomedical studies that use electronic health records, relying solely on labeled data for analysis may be inefficient due to small sample sizes resulting from various resource constraints. In a clinical trial setting, physicians may need to interpret evidence from a randomized controlled trial consisting of patients whose demographics and other historical characteristics are quite different from their own patients. Similarly, researchers studying a pneumonia outbreak during the flu season may find a predictive model developed during the non-flu season to be relevant and useful. In all of these scenarios, it is crucial to develop methods that can appropriately incorporate information from one population into statistical analyses for another. This project will develop a suite of statistically sound methods that can effectively integrate external data into primary studies. The research product has the potential to be applied to various fields, such as Alzheimer's disease, mental health disorders, cancer, and pain research. The project also contains active mentoring plans at both disciplinary and interdisciplinary levels, benefiting local high school students, undergraduates, master's and PhD students, as well as biomedical investigators.In this project, the unique combination of semiparametric statistics, robust statistical methods, statistical learning techniques, missing data analysis, and high-dimensional data analysis will be leveraged to develop a suite of statistically sound methods for incorporating external data into primary studies. The new methods have minimal model assumptions: they are either developed under an assumption lean framework or allow for misspecification of more than one nuisance model in the procedure. Compared to naive methods that do not incorporate external data, the new methods are guaranteed to increase estimation efficiency, improve statistical power, and enhance scientific discovery. Moreover, they achieve maximum efficiency gains when the nuisance models are correctly specified.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.