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      Evaluating Random Forests for Survival Analysis using Prediction Error Curves.

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          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. Reproducible results on the user level are given for publicly available data from the German breast cancer study group.

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          Author and article information

          Journal
          J Stat Softw
          Journal of statistical software
          Foundation for Open Access Statistic
          1548-7660
          1548-7660
          Sep 2012
          : 50
          : 11
          Affiliations
          [1 ] Department of Biostatistics, University of Copenhagen, Denmark.
          [2 ] Department of Epidemiology and Public Health, University of Miami, USA.
          Article
          NIHMS589222
          10.18637/jss.v050.i11
          4194196
          25317082
          447616d4-6bb8-423d-a054-d6dc25177882

          R.,Survival prediction,prediction error curves,random survival forest

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