The analysis of survival data

生存数据的分析对流行病学、统计和预防研究部的研究以及整个生物医学研究都很有意义，因为遇到的数据通常是这种性质的。我们在这个项目中的目标是多方面的： (1)发展了非标准型不完全事件的时间分析方法，如随机截断、当前状态和区间截尾等。已有大量文献用于处理随机右删失的数据，然而，需要更多的努力来开发方法来处理频繁发生的其他类型的不完备性。例如，在许多回溯性怀孕研究中经常观察到随机截断，其中只有在研究开始时怀孕的女性才被纳入研究。 (2)发展处理多变量生存数据的方法。该项目的这一部分是由于许多流行病学研究的需要而需要的，其中感兴趣的不仅仅是对一个感兴趣的简单事件进行建模，而是对一系列复杂事件进行建模。这一部分的重点是开发分析多阶段数据、重复事件和竞争风险数据等多变量生存数据的方法。这种多变量数据的例子可以在Despr、NICHD等生命研究和其他预期怀孕研究、安全分娩等方面进行的各种研究中找到。这里开发的方法将解决一些问题，如对不同阶段的分娩进程进行建模，对怀孕到分娩或流产的时间进行建模。此外，出于需要更好地了解与反复遭受与妊娠丢失相关的不良后果的妇女相关的风险因素，我们打算使用竞争风险数据和反复事件数据来研究对此类事件的建模。

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