LEAPS-MPS: Flexible Semiparametric Transformation Models with Applications to Personalized Medicine

LEAPS-MPS：灵活的半参数转换模型及其在个性化医疗中的应用

• 批准号: 2316868
• 负责人: Yinghao Pan
• 金额: $ 22.97万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316868&HistoricalAwards=false
• 关键词: LEAPS; MPS; Flexible; Semiparametric; Transformation

Censored data arise when the failure time of interest is only partially observed. This project will develop novel and powerful semiparametric transformation models to more effectively analyze such data. Furthermore, this project aims to contribute to the understanding and advancement of personalized medicine, a field focused on tailoring medical decisions and interventions to individual patients. Although primarily motivated by biomedical applications, such as HIV vaccine and AIDS treatment, the outcomes of this research will have broad applications in diverse disciplines, including engineering, economics, finance, and social sciences. The PI will establish new summer research programs designed to increase the participation of underrepresented groups in undergraduate research in statistics and prepare them for success in advanced degree programs and careers in academia and industry. Additionally, the project will offer research training opportunities at the graduate level. The new models proposed in this research extend the traditional transformation models in important directions and effectively bridge the gap between additive-multiplicative and transformation models, two of the most prominent models in survival analysis. The presence of infinite dimensional parameters poses considerable computational and theoretical challenges. To address these challenges, this project will develop robust and scalable Expectation-Solving algorithms for estimating model parameters under various censoring mechanisms, including right-censored, interval-censored, and partly interval-censored. Moreover, a new class of semiparametric transformation models will be proposed for estimating optimal treatment regimes, an important topic in personalized medicine. This research will leverage modern empirical process theory to develop new theoretical arguments for investigating the proposed estimators. Simulation studies and real data analysis on HIV vaccine and AIDS clinical trials will be performed to demonstrate the methods. The PI will also create and distribute user-friendly open-source software to researchers and practitioners in the field.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.

当仅部分观察到感兴趣的故障时间时，就会出现删失数据。该项目将开发新的和强大的半参数转换模型，以更有效地分析此类数据。此外，该项目旨在促进对个性化医疗的理解和发展，个性化医疗是一个专注于为个别患者量身定做医疗决策和干预措施的领域。尽管这项研究的主要动机是生物医学应用，如艾滋病毒疫苗和艾滋病治疗，但这项研究的成果将在不同的学科中有广泛的应用，包括工程、经济、金融和社会科学。PI将建立新的暑期研究计划，旨在增加未被充分代表的群体对统计学本科生研究的参与，并为他们在学术界和工业界的高级学位课程和职业生涯中取得成功做好准备。此外，该项目还将提供研究生水平的研究培训机会。本研究提出的新模型在重要方向上扩展了传统的转换模型，有效地弥补了生存分析中最突出的两个模型--加法-乘法模型和转换模型之间的差距。无限维参数的存在带来了相当大的计算和理论挑战。为了应对这些挑战，本项目将开发稳健和可扩展的期望求解算法，用于在各种审查机制下估计模型参数，包括右审查、区间审查和部分区间审查。此外，还将提出一类新的半参数变换模型来估计最优治疗方案，这是个性化医学中的一个重要课题。这项研究将利用现代经验过程理论，为研究拟议的估计量发展新的理论论点。将对HIV疫苗和艾滋病临床试验进行模拟研究和实际数据分析，以验证方法的有效性。PI还将创建并向该领域的研究人员和从业者分发用户友好的开源软件。这一奖项反映了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。