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: 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=713535
• 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，越来越多的患者经历了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被治愈的对象。先进的技术也会产生各种形式的大数据，比如癌症生物学研究中的组织微阵列数据。在卫生服务研究中广泛使用观察性行政数据库也给大数据时代的因果推理带来了挑战。