Advanced Regression and Prediction Methods in Event History Data Analysis

事件历史数据分析中的高级回归和预测方法

• 批准号: RGPIN-2020-05803
• 负责人: Zhu, Yayuan
• 金额: $ 1.31万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717799
• 关键词: Advanced; Regression; Prediction; Methods; Event

Event risk prediction is important in many fields, which facilitates early prevention, decision making and resource planning. For example, risk prediction of the relapse of leukemia after first remission is helpful to guide treatment before clinical symptoms appear; prediction of the likelihood of insurance claims can aid assessing cost and estimating premium. Time to an event endpoint is often studied as the outcome in economics, finance, engineering, medical science, social science and environmental science, e.g. time to cancer relapse, time to the failure of a product, time to the initiation of an auto insurance claim, etc. Longitudinal data on risk factors associated with time-to-event outcomes create a dynamic data environment in which prognostic information is updated over time with adapting to the changing risk sets. For example, in long-term disease management, patients are often followed up by recurrent clinic visits where physician evaluation and lab tests are performed and data are collected. This time-varying information provides individual characteristics related to disease activity, responses to therapies, and other clinical history that could notably change over time, and thus promotes the development of dynamic prognostic models. In this application, I will discuss the dynamic prediction of event risks by using longitudinal predictor variables and propose statistical methodologies accordingly. Prediction of the risk of a clinical event (e.g. disease relapse, progression to end stage) using longitudinal biomarkers will be used as an illustration. The well-known challenges arising from practice are intermittent measurements of longitudinal biomarkers due to patients' non-adherence to scheduled visit times, unsynchronized measurement schemes of multiple markers, multistate transition during disease progression, etc. My ultimate goal is to develop novel statistical methodologies to solve diverse and complex real data problems and accomplish well-performing prediction of individual event risks. Broad collaboration networks will be sought in the meantime to apply the proposed methods to various areas in real life. Specific research aims are proposed in the research plan as short-term goals. The methodologies I propose across survival data analysis, longitudinal data analysis, and functional data analysis, etc. Prognostic models are mainly constructed by landmarking or joint modeling. Computationally efficient and easily implementable methods are highly desirable so as to handle increasingly big and complicated data sets that may include electronic health records, genomic data, imaging data, internet-based data sources, and data provided by digital monitoring devices. Although the discussion about clinical events is focused in this proposal for illustration, the addressed problems exist in many areas in natural science and engineering and the proposed methods are applicable to a variety of practical problems.

事件风险预测在许多领域都很重要，它有助于早期预防，决策和资源规划。例如，白血病首次缓解后复发的风险预测有助于在临床症状出现之前指导治疗;预测保险索赔的可能性有助于评估成本和估计保费。 至事件终点的时间通常作为经济学、金融学、工程学、医学科学、社会科学和环境科学中的结果进行研究，例如，至癌症复发的时间、至产品失效的时间、至启动汽车保险索赔的时间、与达到预期寿命的时间相关的风险因素的纵向数据事件结果创建动态数据环境，其中预测信息随着时间的推移而更新，以适应变化的风险集合。例如，在长期疾病管理中，患者通常通过反复的诊所访问进行随访，其中进行医生评估和实验室测试并收集数据。这种随时间变化的信息提供了与疾病活动、对治疗的反应以及可能随时间显著变化的其他临床病史相关的个体特征，从而促进了动态预后模型的发展。 在这个应用中，我将讨论通过使用纵向预测变量来动态预测事件风险，并提出相应的统计方法。使用纵向生物标志物预测临床事件（例如疾病复发、进展至终末期）的风险将用作说明。从实践中产生的众所周知的挑战是间歇性测量的纵向生物标志物，由于患者的不遵守预定的访问时间，不同步的测量方案的多个标志物，在疾病进展过程中的多状态转换等，我的最终目标是开发新的统计方法，以解决多样化和复杂的真实的数据问题，并完成良好的预测个别事件的风险。与此同时，将寻求广泛的合作网络，以便将所提出的方法应用于真实的生活的各个领域。 研究计划中提出了具体的研究目标，作为短期目标。我提出的方法论横跨生存数据分析、纵向数据分析和功能数据分析等。预后模型主要通过地标或联合建模来构建。非常需要计算高效且易于实现的方法，以便处理日益庞大且复杂的数据集，所述数据集可包括电子健康记录、基因组数据、成像数据、基于互联网的数据源以及由数字监测设备提供的数据。 虽然本建议中针对临床事件的讨论是为了说明，但所解决的问题存在于自然科学和工程的许多领域，所提出的方法适用于各种实际问题。