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Rationale and Applications of Survival Tree and Survival Ensemble Methods

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  • Yan Zhou
  • John McArdle

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. Copyright The Psychometric Society 2015

Suggested Citation

  • Yan Zhou & John McArdle, 2015. "Rationale and Applications of Survival Tree and Survival Ensemble Methods," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 811-833, September.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:3:p:811-833
    DOI: 10.1007/s11336-014-9413-1
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    References listed on IDEAS

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    1. Molinaro, Annette M. & Dudoit, Sandrine & van der Laan, M.J.Mark J., 2004. "Tree-based multivariate regression and density estimation with right-censored data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 154-177, July.
    2. Ciampi, Antonio & Thiffault, Johanne & Nakache, Jean-Pierre & Asselain, Bernard, 1986. "Stratification by stepwise regression, correspondence analysis and recursive partition: a comparison of three methods of analysis for survival data with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 4(3), pages 185-204, October.
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    Cited by:

    1. Beilin Jia & Donglin Zeng & Jason J. Z. Liao & Guanghan F. Liu & Xianming Tan & Guoqing Diao & Joseph G. Ibrahim, 2022. "Mixture survival trees for cancer risk classification," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 356-379, July.
    2. Dimitris Papathanasiou & Konstantinos Demertzis & Nikos Tziritas, 2023. "Machine Failure Prediction Using Survival Analysis," Future Internet, MDPI, vol. 15(5), pages 1-26, April.

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