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.

中文翻译：

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离散时间到事件模型中时变系数的基于树的建模。

危害模型是用于离散时间到事件数据建模的流行工具。特别是两种用于对时间相关效应建模的方法是常用的。更传统的假设是一个线性预测器，解释变量的影响随着时间的推移是恒定的。更灵活的方法使用半参数模型类，允许解释变量的影响随时间平滑变化。这里考虑的方法介于这些建模策略之间。它假设解释变量的影响是分段常数。特别是，它允许评估效果强度在哪些时间点发生变化，并且能够以简单的方式近似估算效果变化的相当复杂的变化。提出了一种基于树的方法来对分段常数时变系数进行建模，它被嵌入到变系数模型的框架中。该方法的一个重要特点是它自动选择相关的解释变量，不需要单独的变量选择程序。在几个模拟研究中研究了该方法的特性，并通过考虑两个实际应用来证明其有用性。