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Tree-based modeling of time-varying coefficients in discrete time-to-event models

机译:离散时间对时间内模型时变系数的基于树的建模

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摘要

Hazard models are popular tools for the modeling of discrete time-to-event data. In particular two approaches for modeling time dependent effects are in common use. The more traditional one assumes a linear predictor with effects of explanatory variables being constant over time. The more flexible approach uses the class of semiparametric models that allow the effects of the explanatory variables to vary smoothly over time. The approach considered here is in between these modeling strategies. It assumes that the effects of the explanatory variables are piecewise constant. It allows, in particular, to evaluate at which time points the effect strength changes and is able to approximate quite complex variations of the change of effects in a simple way. A tree-based method is proposed for modeling the piecewise constant time-varying coefficients, which is embedded into the framework of varying-coefficient models. One important feature of the approach is that it automatically selects the relevant explanatory variables and no separate variable selection procedure is needed. The properties of the method are investigated in several simulation studies and its usefulness is demonstrated by considering two real-world applications.
机译:危险模型是用于建模离散时间数据的流行工具。特别是用于建模时间依赖效果的两种方法都是常用的。传统更传统的假设线性预测器,随着时间的推移,具有解释变量的效果的线性预测。更灵活的方法使用允许解释变量的效果随时间顺利变化的效果的半游戏模型。这里考虑的方法在这些建模策略之间。它假设解释性变量的效果是分段常数。特别地,允许评估效果强度变化的时间点,并且能够以简单的方式近似效果变化变化的变化。提出了一种基于树的方法,用于建模分段恒定的时变系数,该系数嵌入到变化系数模型的框架中。该方法的一个重要特征是它自动选择相关的解释变量,并且不需要单独的变量选择过程。在若干模拟研究中研究了该方法的性质,并通过考虑两个现实世界的应用来证明其有用性。

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  • 来源
    《Lifetime Data Analysis》 |2020年第3期|545-572|共28页
  • 作者单位

    Department of Medical Biometry Informatics and Epidemiology Faculty of Medicine University of Bonn Venusberg-Campus 1 53127 Bonn Germany Institute of General Practice and Family Medicine Faculty of Medicine University of Bonn Venusberg-Campus 1 53127 Bonn Germany;

    Department of Statistics Ludwig-Maximilians-University Munich Ludwigstrasse 33 80539 Munich Germany;

    Department of Oral and Cranio-Maxillo and Facial Plastic Surgery University Hospital Bonn Venusberg-Campus 1 53127 Bonn Germany;

    Institute of General Practice and Family Medicine Faculty of Medicine University of Bonn Venusberg-Campus 1 53127 Bonn Germany;

    Department of Medical Biometry Informatics and Epidemiology Faculty of Medicine University of Bonn Venusberg-Campus 1 53127 Bonn Germany;

    Department of Medical Biometry Informatics and Epidemiology Faculty of Medicine University of Bonn Venusberg-Campus 1 53127 Bonn Germany;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Discrete time-to-event data; Time-varying coefficients; Recursive partitioning; Semiparametric regression; Survival analysis;

    机译:离散的时间 - 事件数据;时变系数;递归分区;Semiparametric回归;生存分析;

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