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Survival models and health sequences.
Lifetime Data Analysis ( IF 1.3 ) Pub Date : 2018-03-03 , DOI: 10.1007/s10985-018-9424-9
Walter Dempsey 1 , Peter McCullagh 2
Affiliation  

Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique—reverse alignment—for constructing statistical models for survival processes, here termed revival models. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes.

中文翻译:

生存模型和健康序列。

生存研究通常不仅会生成每个患者的生存时间,还会在患者还活着的情况下每年或半年检查时生成一系列健康测量结果。这种随机长度的序列伴随着生存时间称为生存过程。健康状况良好通常与较长的生存期相关,因此不能假定生存过程的两个部分是独立的。本文关注的是一种通用技术——反向对齐——用于构建生存过程的统计模型,这里称为复兴模型。复苏模型是一种回归模型,因为它将协变量和治疗效果纳入了生存时间的分布和健康结果的联合分布。复苏模型还根据观察到的历史确定条件生存分布,该分布描述了如何通过观察到的健康结果进展来确定随后的生存分布。
更新日期:2018-03-03
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