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

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和治愈。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对象。先进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。在大数据时代，观察性管理数据库在卫生服务研究中的广泛使用也给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来分析观察和临床研究中出现的经审查的生存数据和纵向数据一直是我们研究计划的主要主题。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)开发模型评估工具和灵活的非参数模型的生存数据与治愈的分数2)适当地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)因果推断，以确定治疗效果与高维管理数据库4)组织微阵列数据和联合模型的预测生物标记物的确定生存和纵向数据。5)开发R包和SAS宏供公众使用建议的模型和方法预计，这项研究将产生新的统计方法与高维临床和观察数据的统计学家工作。新方法还将对其他自然科学和工程领域产生影响，这些领域收集和分析事件时间，如可靠性。这项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研究工作的重复性和适用性，也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等级别的实习生提供合适的机会。

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 fraction2) Properly modeling correlated survival times and variable selection using marginal survival models and marginal cure models with high-dimensional data3) Causal inference to determine treatment effects with high-dimensional administrative databases4) 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 useIt 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.