Truncated survival data and Bayesian optimal design for changepoint problems

变点问题的截断生存数据和贝叶斯最优设计

• 批准号: 37993-2011
• 负责人: Wolfson, David
• 金额: $ 1.53万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=474791
• 关键词: Truncated; survival; data; Bayesian; optimal

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.

我的第一个研究领域是生存分析。 假设观察是随着时间的推移进行的，对象（例如，可以是患者）具有明确定义的开始时间和明确定义的终点。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，患者患有痴呆症的时间长度。生存分析的特点是某些项目的生存时间观察不完全。发生这种情况的最常见原因是，在研究结束时关注的事件尚未发生，或者受试者在事件发生前失访。如果发生这种情况，生存时间被称为右删失。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。然后，生存时间被称为左截断。我擅长分析右删失左截断的生存数据。