Evaluating Random Forests for Survival Analysis using Prediction Error Curves.
Evaluating Random Forests for Survival Analysis using Prediction Error Curves.
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DOI：
10.18637/jss.v050.i11
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发表时间：
2012-09
影响因子：
5.8
通讯作者：
Gerds TA
中科院分区：
文献类型：
--
作者：
Mogensen UB;Ishwaran H;Gerds TA
预测误差曲线越来越多地用于评估和比较生存分析中的预测。本文介绍了R包pec，它提供了一组用于有效计算预测误差曲线的函数。该软件实现了截尾权重的逆概率来处理右截尾数据，并实现了交叉验证的几种变体来处理表观误差问题。原则上，可以评估所有类型的预测模型，并且该软件包很容易支持大多数传统的回归建模策略，如考克斯回归或加性风险回归，以及最先进的机器学习方法，如随机森林，一种非参数方法，在低维和高维设置中为传统策略提供了有前途的替代方案。我们展示了如何将pec的功能扩展到尚未得到支持的预测模型。作为一个例子，我们实现了对基于R包randomSurvivalForest和party的随机森林预测模型的支持。使用哥本哈根中风研究的数据，我们使用PEC比较随机森林的逐步变量选择的考克斯回归模型。德国乳腺癌研究组的公开数据给出了用户层面的可再现结果。
Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error curves. The software implements inverse probability of censoring weights to deal with right censored data and several variants of cross-validation to deal with the apparent error problem. In principle, all kinds of prediction models can be assessed, and the package readily supports most traditional regression modeling strategies, like Cox regression or additive hazard regression, as well as state of the art machine learning methods such as random forests, a nonparametric method which provides promising alternatives to traditional strategies in low and high-dimensional settings. We show how the functionality of pec can be extended to yet unsupported prediction models. As an example, we implement support for random forest prediction models based on the R-packages randomSurvivalForest and party. Using data of the Copenhagen Stroke Study we use pec to compare random forests to a Cox regression model derived from stepwise variable selection. Reproducible results on the user level are given for publicly available data from the German breast cancer study group.