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Boosting multi-state models

机译:促进多状态模型

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One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.
机译:多状态建模的一个重要目标是通过估计解释变量的影响来探索有关条件过渡类型特定风险率函数的信息。如果假定这些协变量效应在过渡类型之间不同,则可以使用单个特定于过渡类型的模型来执行此操作。为了研究此假设是否成立,或者在几种过渡类型之间的影响之一是否相等(交叉过渡类型的影响),必须使用组合模型,例如使用分层的部分似然公式。在这里,关于潜在的协变量效应机制的先验知识通常很少,尤其是关于过渡类型特异性或交叉转化类型效应的无效性。因此,数据驱动的变量选择是一项重要任务:如果对所有过渡类型进行联合建模,则必须考虑大量可估计的影响。一个相关但后续的任务是模型选择:假设线性是一种效果,是否可以令人满意地估算出效果,还是真正的内在本质严重偏离了线性?本文介绍了用于多状态模型的按组件的功能梯度下降提升(短提升),一种在单个估计运行中同时执行无监督变量选择和模型选择的方法。我们证明,在经典回归场景中引入和说明的增强应用中的功能和优势仍然存在于向多状态模型的转移中。结果,增强提供了一种有效的方法,可以回答有关单个过渡类型特有或交叉过渡类型效果的无效性和非线性的问题。

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