Truncated survival data and Bayesian optimal design for changepoint problems

我的第一个研究领域是生存分析领域。假设观察是随着时间的推移进行的，对具有明确定义的开始时间和结束点的项目(例如，他们可能是患者)进行观察。例如，起始时间可以是痴呆症的发病日期，终点可以是死亡。人们感兴趣的是对所谓的“生存时间”--从开始到结束之间的时间--进行统计推断；例如，痴呆症患者的生存时间。生存分析的特点是某些项目的生存时间是不完全观察的。最常见的情况是，要么是因为研究结束时感兴趣的事件还没有发生，要么是因为受试者在事件发生之前已经失去了后续行动。如果发生这种情况，据说生存时间是经过正确审查的。然而，有时由于研究设计的原因，存活时间较短的受试者根本没有被观察到作为研究的一部分。然后，生存时间被认为是被截断的。我专门分析被右删减和左截断的生存数据。

My first area of research is in the area of survival analysis. Suppose that observations are taken over time, on items (they could be patients, for example) with a well-defined initiation time and a well defined end point. For example, the initiation time could be the onset date of dementia and the endpoint could be death. Interest lies in making statistical inference about the so-called "survival time"-between initiation and the endpoint; for example, the length of time that patients live with dementia. The feature of survival analysis is that some of the survival times for the items are incompletely observed. Most often this happens because either the event of interest has not yet occurred by the time the study ends or the subjects has been lost to follow-up before the event has occurred. If this happens the survival times are said to be right censored. Sometimes however, as a result of the study design, subjects who have short survival times are simply not observed as part of the study. The survival times are then said to be left truncated. I specialize in analyzing survival data that are right censored and left truncated.