A generalized additive model approach to time-to-event analysis
A generalized additive model approach to time-to-event analysis
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DOI：
10.1177/1471082x17748083
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发表时间：
2018-06-01
影响因子：
1
通讯作者：
Scheipl, Fabian
中科院分区：
文献类型：
--
作者：
Bender, Andreas;Groll, Andreas;Scheipl, Fabian
本教程文章演示了如何利用最近为广义加法混合模型开发的高级推理方法，以非常灵活的方式对事件时间数据进行建模。特别是，我们描述了必要的预处理步骤，将这些数据转换成一个合适的格式，并显示了各种影响，包括一个平滑的非线性基线的危险，以及潜在的非线性和非线性随时间变化的影响，可以估计和解释。我们还提出了有用的图形工具模型评估和解释的估计效果。在整个过程中，我们使用各种应用程序的例子来演示这种方法。本文附带了一个名为pammtools的新R包，它实现了这里描述的所有工具。
This tutorial article demonstrates how time-to-event data can be modelled in a very flexible way by taking advantage of advanced inference methods that have recently been developed for generalized additive mixed models. In particular, we describe the necessary pre-processing steps for transforming such data into a suitable format and show how a variety of effects, including a smooth nonlinear baseline hazard, and potentially nonlinear and nonlinearly time-varying effects, can be estimated and interpreted. We also present useful graphical tools for model evaluation and interpretation of the estimated effects. Throughout, we demonstrate this approach using various application examples. The article is accompanied by a new R-package called pammtools implementing all of the tools described here.