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Rationale and applications of survival tree and survival ensemble methods. (English) Zbl 1323.62127
Summary: 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.
##### MSC:
 62P15 Applications of statistics to psychology 62P10 Applications of statistics to biology and medical sciences; meta analysis 68T05 Learning and adaptive systems in artificial intelligence
##### Software:
ipred; party; partykit; randomForest; randomSurvivalForest; rpart
Full Text:
##### References:
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