Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds
Title: Evaluating Random Forests for Survival Analysis Using Prediction Error Curves
Abstract: 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.

Page views:: 13880. Submitted: 2011-02-28. Published: 2012-09-18.
Paper: Evaluating Random Forests for Survival Analysis Using Prediction Error Curves     Download PDF (Downloads: 29209)
pec_2.2.2.tar.gz: R source package Download (Downloads: 698; 1MB)
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v50i11.Rout: R example output Download (Downloads: 718; 16KB)

DOI: 10.18637/jss.v050.i11

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.