Truncated survival data and Bayesian optimal design for changepoint problems

我的第一个研究领域是生存分析。假设随着时间的推移，对具有明确定义的起始时间和明确定义的终点的项目（例如，可以是患者）进行观察。例如，起始时间可以是痴呆的发病日期，终点可以是死亡。兴趣在于对所谓的“生存时间”进行统计推断——从开始到终点；例如，痴呆症患者的生存时间长短。生存分析的特点是，有些项目的生存时间是不完全观察到的。大多数情况下，这是因为在研究结束时，感兴趣的事件尚未发生，或者在事件发生之前，受试者已经失去了随访。如果发生这种情况，生存时间被认为是正确的审查。然而，有时由于研究设计的原因，生存时间短的受试者根本没有被观察到作为研究的一部分。因此，生存时间就被缩短了。我的专长是分析右删减左删减的生存数据。

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.