Subscribe to DSC Newsletter

Evaluating RF for Survival Analysis Using Prediction Error Curves

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23.


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 highdimensional 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.

Read full paper at http://bit.ly/1yahoX1

Views: 503

Tags: Analysis, Learning, Machine, Statistics, Survival

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Videos

  • Add Videos
  • View All

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service