Statistical models and methods for survival and longitudinal data in clinical and observational studies

临床和观察研究中生存和纵向数据的统计模型和方法

• 批准号: RGPIN-2018-05197
• 负责人: Peng, Yingwei
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=661908
• 关键词: Statistical; models; methods; survival; longitudinal

Due to advances in cancer treatment research, more patients are experiencing longer survival and becoming cured. There are increasing demands for appropriate statistical methods to take the cured subjects into account in various complicated sampling designs and models assumptions. Advanced technology also generates big data in various forms, such as tissue microarray data in cancer biology studies. The wide use of observational administrative databases for health service research also creates challenges in causal inferences in the era of big data.******Developing novel statistical models, estimation methods, and accompanying software for analyzing censored survival data and longitudinal data arising in observational and clinical studies has been the primary topic of our research program. We will continue this research program with focus on issues including, but not limited to:******1) Development of model assessment tools and flexible nonparametric models for survival data with a cured fraction******2) Properly modeling correlated survival times and variable selection using marginal survival models and marginal cure models with high-dimensional data******3) Causal inference to determine treatment effects with high-dimensional administrative databases******4) Predictive biomarkers determination for tissue microarray data and joint models for survival and longitudinal data.******5) Development of R packages and SAS macros for proposed models and methods for public use******It is anticipated that this research will generate new statistical methods for statisticians working with high-dimensional clinical and observational data. The new methods will also have impact on other fields of natural science and engineering where event times are collected and analyzed, such as reliability. The research stimulates not only new development in the statistics models and theory, but also knowledge transfer from statistical models to statistical applications due to the development of software for public use to enhance reproducibility and applicability of the proposed research work. The research program will also provide suitable opportunities for trainees at levels from masters students to post-doctoral fellows.

由于癌症治疗研究的进步，更多的患者正在经历更长的生存期并被治愈。在各种复杂的抽样设计和模型假设中，越来越需要适当的统计方法来考虑治愈的受试者。先进的技术还产生各种形式的大数据，例如癌症生物学研究中的组织微阵列数据。观察性行政数据库在卫生服务研究中的广泛使用也给大数据时代的因果推断带来了挑战。开发新的统计模型，估计方法和配套软件，用于分析观察和临床研究中出现的删失生存数据和纵向数据，一直是我们研究计划的主要课题。我们将继续这项研究计划，重点关注的问题包括，但不限于：******1）模型评估工具和灵活的非参数模型的生存数据与治愈分数的发展 ******2）正确建模相关的生存时间和变量选择使用边缘生存模型和边缘治愈模型与高维数据 ******3）因果推理，以确定治疗效果与高维管理数据库 *******4）预测生物标志物的组织微阵列数据和联合模型的生存和纵向数据。5)为公众使用的拟议模型和方法开发R包和SAS宏 * 预计这项研究将为处理高维临床和观察数据的统计学家产生新的统计方法。新方法也将对收集和分析事件时间的其他自然科学和工程领域产生影响，例如可靠性。该研究不仅促进了统计模型和理论的新发展，而且由于开发了供公众使用的软件，以提高拟议的研究工作的可重复性和适用性，还促进了从统计模型到统计应用的知识转移。该研究计划还将为从硕士生到博士后研究员的培训人员提供合适的机会。