Abstract As data sets continue to increase in size, there is growing interest in methods for prediction that are both flexible and interpretable. A flurry of recent work on this topic has focused on additive modeling in the regression setting, and in particular, on the use of data-adaptive non-linear functions that can be used to flexibly model each covariate’s effect, conditional on the other features in the model. In this paper, we extend this recent line of work to the survival setting. We develop an additive Cox proportional hazards model, in which each additive function is obtained by trend filtering, so that the fitted functions are piece-wise polynomial with adaptively-chosen knots. An efficient proximal gradient descent algorithm is used to fit the model. We demonstrate its performance in simulations and in application to a primary biliary cirrhosis data set, as well as a data set consisting of time to publication for clinical trials in the biomedical literature.

中文翻译：

---

用于生存数据的灵活且可解释的模型

摘要 随着数据集规模的不断增加，人们对既灵活又可解释的预测方法越来越感兴趣。最近关于这个主题的一系列工作集中在回归设置中的加法建模上，特别是使用数据自适应非线性函数，这些函数可用于灵活地对每个协变量的影响进行建模，条件是该模型。在本文中，我们将最近的这项工作扩展到生存环境。我们开发了一个加法 Cox 比例风险模型，其中每个加法函数都是通过趋势过滤获得的，因此拟合函数是具有自适应选择节点的分段多项式。使用有效的近端梯度下降算法来拟合模型。