Han Gang, Schell Michael J, Kim Jongphil
Department of Biostatistics, Yale University School of Public Health, 60 College Street, New Haven, CT 06520, U.S.A.
Stat Med. 2014 Jan 15;33(1):59-73. doi: 10.1002/sim.5915. Epub 2013 Jul 30.
Statistical models for survival data are typically nonparametric, for example, the Kaplan-Meier curve. Parametric survival modeling, such as exponential modeling, however, can reveal additional insights and be more efficient than nonparametric alternatives. A major constraint of the existing exponential models is the lack of flexibility due to distribution assumptions. A flexible and parsimonious piecewise exponential model is presented to best use the exponential models for arbitrary survival data. This model identifies shifts in the failure rate over time based on an exact likelihood ratio test, a backward elimination procedure, and an optional presumed order restriction on the hazard rate. Such modeling provides a descriptive tool in understanding the patient survival in addition to the Kaplan-Meier curve. This approach is compared with alternative survival models in simulation examples and illustrated in clinical studies.
生存数据的统计模型通常是非参数的，例如Kaplan-Meier曲线。然而，参数生存建模，如指数建模，可以揭示更多见解，并且比非参数方法更有效。现有指数模型的一个主要限制是由于分布假设而缺乏灵活性。提出了一种灵活且简约的分段指数模型，以最佳地将指数模型用于任意生存数据。该模型基于精确似然比检验、向后消除程序以及对危险率的可选假定顺序限制来识别随时间变化的失效率变化。除了Kaplan-Meier曲线之外，这种建模还提供了一种理解患者生存情况的描述工具。在模拟示例中将该方法与其他生存模型进行了比较，并在临床研究中进行了说明。
