Improved survival modeling in cancer research using a reduced piecewise exponential approach.

Improved survival modeling in cancer research using a reduced piecewise exponential approach.
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DOI:
10.1002/sim.5915
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发表时间:
2014-01-15
影响因子:
2
通讯作者:
Kim, Jongphil
Kim, Jongphil
中科院分区:
医学3区
文献类型:
--
作者:
Han, Gang;Schell, Michael J.;Kim, Jongphil

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生存数据的统计模型通常是非参数的,例如卡普兰-迈耶曲线。然而,参数生存模型(例如指数模型)可以揭示更多见解,并且比非参数替代方案更有效。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,以便最好地将指数模型用于任意生存数据。该模型根据精确的似然比测试、向后消除程序以及对危险率的可选假定顺序限制来识别故障率随时间的变化。除了 Kaplan-Meier 曲线之外,这种建模还提供了了解患者生存情况的描述性工具。在模拟示例中将这种方法与替代生存模型进行比较,并在临床研究中进行说明。
Statistical models for survival data are typically nonparametric, e.g., 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.
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