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Rationale and Applications of Survival Tree and Survival Ensemble Methods

Published online by Cambridge University Press:  01 January 2025

Yan Zhou  and
John J. McArdle
Yan Zhou*
Affiliation:
University of California, Los Angeles
John J. McArdle
Affiliation:
University of Southern California
*
Correspondance should be sent to Yan Zhou, Mary S. Easton Center for Alzheimer’s Disease Research, Department of Neurology, University of California, Los Angeles, 10911 Weyburn Avenue, Suite 200, Los Angeles, CA 90095, USA. E-mail: YanZhou@mednet.ucla.edu
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Abstract

Classification and Regression Trees (CART), and their successors—bagging and random forests, are statistical learning tools that are receiving increasing attention. However, due to characteristics of censored data collection, standard CART algorithms are not immediately transferable to the context of survival analysis. Questions about the occurrence and timing of events arise throughout psychological and behavioral sciences, especially in longitudinal studies. The prediction power and other key features of tree-based methods are promising in studies where an event occurrence is the outcome of interest. This article reviews existing tree algorithms designed specifically for censored responses as well as recently developed survival ensemble methods, and introduces available computer software. Through simulations and a practical example, merits and limitations of these methods are discussed. Suggestions are provided for practical use.

Keywords

survival treesrandom forestssurvival analysisstatistical learningrecursive partitioning
Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 3 , September 2015 , pp. 811 - 833
Copyright
Copyright © 2014 The Psychometric Society

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Footnotes

Electronic Supplementary Material The online version of this article (doi:10.1007/s11336-014-9413-1) contains supplementary material, which is available to authorized users.

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