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AN EXTENSION OF GENERALIZED BAYESIAN ENSEMBLE TREE MODELS TO SURVIVAL ANALYSIS
Keywords:
survival analysis, bootstrap, Bayesian non-parametric learning, ensemble modelsDOI:
https://doi.org/10.17654/0972086323007Abstract
The analysis of small datasets is a relevant problem in a lot of different fields, especially in medicine, and the use of appropriate resampling methods could provide better and more reliable results.
In this work, a new bagging survival tree model is proposed, where an extension of Efron’s bootstrap procedure is replaced in the classical model. The proper Bayesian bootstrap allows to enrich the original feature space with new observations sampled from a prior distribution, that are not already present in the original data.
Empirical results are shown through a sensitivity analysis and in a simulated study. The proposed model reaches competitive performances with respect to classical survival models (Cox model and survival random forest) in terms of integrated Brier score with higher stability. The biggest improvements are shown when small sample sizes are involved.
Received: March 7, 2023
Accepted: April 22, 2023
Published: May 26, 2023
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