Dynamic Modeling and Risk Prediction with Complex Observational Semi-Competing Risks Data

利用复杂的观察性半竞争风险数据进行动态建模和风险预测

• 批准号: 2406910
• 负责人: Hong Zhu
• 金额: $ 17.5万
• 依托单位: University of Virginia Main Campus
• 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: 2023-11-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2406910&HistoricalAwards=false
• 关键词: Dynamic; Modeling; Risk; Prediction; Complex

Observational studies using secondary data sources, such as registry, claims, and electronic health records, are primary research tools to assess treatment effects and predict disease outcomes in real-world settings. Observational data, however, often present many complexities, for which substantial methods development is needed to obtain valid results. Particularly, semi-competing risks data arise when a terminal event (e.g., death) can prevent the observation of a non-terminal event (e.g., cancer recurrence), but not vice versa. The analysis of such data is further complicated with clustered outcomes, time-varying treatment effects, confounding, and dynamic prediction. This project will develop novel dynamic modeling and risk prediction methods with complex observational semi-competing risks data. The project is motivated by cancer studies. The developed methods will also be broadly applicable in other health conditions, reliability studies, and social science, where such data commonly arise. The investigator will integrate research and education by training graduate students, designing advanced topic courses, and engaging underrepresented minority students. The investigator will also develop open-source, user-friendly software packages in R to disseminate the results.The project has three research aims. The first aim is to develop a copula-based, time-varying coefficient, random-effect model for multilevel semi-competing risks data. The second aim is to develop a propensity score matching based method to control for confounding in multilevel observational semi-competing risks data. The impact of omitting unmeasured confounders will be studied. The third aim is to develop a novel dynamic risk prediction tool for non-terminal and terminal events with such data. Unlike traditional prediction models, the developed model will utilize data on patients’ dynamic disease progression and characteristics. The investigator will derive large sample properties of the new estimators, conduct simulations for evaluation, and apply the methods to analyze real-world 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.

使用二级数据源（如登记、索赔和电子健康记录）的观察性研究是评估治疗效果和预测现实环境中疾病结局的主要研究工具。然而，观测数据往往呈现出许多复杂性，因此需要大量的方法开发以获得有效的结果。特别是，当终末事件（例如，死亡）可以阻止观察到非终末事件（例如，癌症复发），但反之亦然。这些数据的分析进一步复杂化，包括聚类结果、随时间变化的治疗效果、混杂和动态预测。该项目将开发新的动态建模和风险预测方法与复杂的观测半竞争性风险数据。该项目的动机是癌症研究。开发的方法也将广泛适用于其他健康状况，可靠性研究和社会科学，这些数据通常出现。调查员将通过培训研究生，设计高级主题课程以及吸引代表性不足的少数民族学生来整合研究和教育。研究员亦会以R语言开发开放源码、方便使用的软件包，以传播研究结果。第一个目标是建立一个基于Copula的、时变系数的、随机效应的多水平半竞争风险数据模型。第二个目的是开发一种基于倾向评分匹配的方法来控制多水平观察性半竞争风险数据中的混杂因素。将研究省略未测量混杂因素的影响。第三个目标是开发一个新的动态风险预测工具，为非终端和终端事件与这些数据。与传统的预测模型不同，开发的模型将利用患者动态疾病进展和特征的数据。研究人员将推导新估算器的大样本特性，进行模拟评估，并应用该方法分析真实世界的数据。该奖项反映了NSF的法定使命，并被认为值得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。