摘要翻译：
许多研究人员将首次击打次数作为生存数据的模型进行了研究。从Wiener过程到Markov链，许多随机过程中都会自然出现首次命中时间。在生存环境中，潜在过程的状态代表了一个项目的强度或一个人的健康。当过程首次达到不利阈值状态时，项目失败或个人经历临床终点。时间尺度可以是日历时间或其他退化或疾病进展的操作度量。在许多应用中，过程是潜在的（即不可观察的）。阈值回归是指具有回归结构的首次命中时间模型，该模型可以容纳协变量数据。过程的参数，阈值状态和时间尺度可能依赖于协变量。本文回顾了这一主题的各个方面，并讨论了未来研究的富有成效的途径。
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英文标题：
《Threshold Regression for Survival Analysis: Modeling Event Times by a
  Stochastic Process Reaching a Boundary》
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作者：
Mei-Ling Ting Lee, G. A. Whitmore
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research.
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PDF链接：
https://arxiv.org/pdf/708.0346