caa a22 4500 60624199X CHVBK 20210128101316.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s11336-014-9413-1 doi (NATIONALLICENCE)springer-10.1007/s11336-014-9413-1 Rationale and Applications of Survival Tree and Survival Ensemble Methods [Elektronische Daten] [Yan Zhou, John McArdle] 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. The Psychometric Society, 2014 survival trees nationallicence random forests nationallicence survival analysis nationallicence statistical learning nationallicence recursive partitioning nationallicence Zhou Yan Mary S. Easton Center for Alzheimer's Disease Research, Department of Neurology, University of California, Los Angeles, 10911 Weyburn Avenue, Suite 200, 90095, Los Angeles, CA, USA aut McArdle John Department of Psychology, University of Southern California, Los Angeles, CA, USA aut Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 811-833 0033-3123 80:3<811 2015 80 11336 https://doi.org/10.1007/s11336-014-9413-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11336-014-9413-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhou Yan Mary S. Easton Center for Alzheimer's Disease Research, Department of Neurology, University of California, Los Angeles, 10911 Weyburn Avenue, Suite 200, 90095, Los Angeles, CA, USA aut NATIONALLICENCE 700 1- McArdle John Department of Psychology, University of Southern California, Los Angeles, CA, USA aut NATIONALLICENCE 773 0- Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 811-833 0033-3123 80:3<811 2015 80 11336