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

使用二手数据源的观察性研究，如登记、索赔和电子健康记录，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，为此需要开发大量的方法来获得有效的结果。特别是，当一个终末事件（如死亡）可以阻止观察到一个非终末事件（如癌症复发）时，就会出现半竞争风险数据，反之则不然。这些数据的分析由于聚类结果、时变治疗效果、混淆和动态预测而变得更加复杂。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风险数据。该项目是由癌症研究推动的。所开发的方法也将广泛适用于其他卫生条件、可靠性研究和社会科学等通常出现此类数据的领域。研究者将通过培训研究生、设计高级主题课程和吸引未被充分代表的少数民族学生来整合研究和教育。研究人员还将用R语言开发开源、用户友好的软件包来传播研究结果。该项目有三个研究目标。第一个目标是为多级半竞争风险数据建立一个基于copula的时变系数随机效应模型。第二个目标是开发一种基于倾向评分匹配的方法来控制多层次观察半竞争风险数据中的混淆。将研究忽略未测量混杂因素的影响。第三个目标是利用这些数据开发一种新的非终端和终端事件动态风险预测工具。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。研究人员将获得新估计器的大样本特性，进行评估模拟，并应用方法分析现实世界的数据。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。

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.