The analysis of survival data

生存数据分析

• 批准号: 10684577
• 负责人: Rajeshwari Sundaram
• 金额: $ 3.83万
• 依托单位: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684577
• 关键词: Address; Biomedical Research; Child; Complex; Data; Enrollment; Epidemiology; Event; Goals; Literature; Methods; Modeling; Names; National Institute of Child Health and Human Development; Nature; Pregnancy; Pregnancy loss; Prevention Research; Recurrence; Risk; Risk Factors; Sexual Maturation; Statistical Methods; Stress; Study models; Survival Analysis; Time; Woman; adverse outcome; epidemiology study; interest; novel; pregnant; prospective; statistics; time-to-pregnancy

The analysis of survival data is of considerable interest to studies in Division of Epidemiology, Statistics and Prevention Research and also to biomedical research in general as the data encountered is typically of that nature. Our objective in this project is multifold: (1) Develop methods to analyze time to an event which have non-standard type of incompleteness, that are beyond right censoring, like random truncation, current status and interval censoring. Extensive literature exists for dealing with data which are randomly right censored However, more effort is needed to develop methods to deal with other type of incompleteness which occur frequently. For example, random truncation is frequently observed in many retrospective pregnancy studies where women are enrolled in the study only if they have gotten pregnant by start of study. (2) Develop methods to deal with multivariate survival data. This part of the project is necessitated by needs of many epidemiological studies, where the interest is not just in modeling one simple event of interest, but rather a host of complex events. The focus of this part is to develop methods to analyze multivariate survival data like the multistage data, recurrent events and competing risks data. Examples of such multivariate data can be found in various studies being conducted in DESPR, NICHD like LIFE study and other prospective pregnancy studies, Safe Labor to name few. The methods developed here will address questions like modeling of progression of labot through various stages, modeling time to pregnancy to delivery or loss. Also, motivated by need to better understand the risk factors associated with women who suffer repeated adverse outcomes associated with pregnancy loss, we intend to study modeling such events using both the competing risks data as well as recurrent events data

生存数据的分析对于流行病学，统计学和预防研究的研究以及一般的生物医学研究的研究非常重要，因为遇到的数据通常是这种性质。我们在这个项目中的目标是多重的​​： （1）开发分析时间的方法，该事件具有非标准类型的不完整性，这些事件超出了右审查，例如随机截断，当前状态和间隔审查。 存在广泛的文献来处理随机正确审查的数据，但是，需要更多的精力来开发处理其他经常发生的其他类型的不完整的方法。例如，在许多回顾性怀孕研究中，经常观察到随机截断，在研究中，只有在研究开始时才能在研究中招募妇女。 （2）开发处理多元生存数据的方法。许多流行病学研究的需求需要该项目的这一部分，在这种研究中，兴趣不仅在于对一个简单的感兴趣事件进行建模，而且还需要一系列复杂的事件。该部分的重点是开发分析多元生存数据（例如多阶段数据，经常性事件和竞争风险数据）的方法。在DESPR，NICHD（如生命研究）和其他前瞻性妊娠研究（安全的劳动）中，可以找到此类多元数据的示例。这里开发的方法将解决诸如Labot通过各个阶段进行建模的问题，将怀孕时间建模到分娩或损失。此外，由于需要更好地了解与遭受与妊娠丧失相关的反复不良结果相关的危险因素的动机，我们打算使用竞争风险数据以及经常性事件数据来研究对此类事件进行建模