Experimental Comparison of Ensemble Methods and Time-to-Event Analysis Models Through Integrated Brier Score and Concordance Index

Camila Fernandez (1) , Chung Shue Chen (2) , Pierre Gaillard (3) , Alonso Silva (2)

1 LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation
2 Nokia Bell Labs [Paris-Saclay]
3 Thoth - Apprentissage de modèles à partir de données massives

Time-to-event analysis is a branch of statistics that has increased in popularity during the last decades due to its many application fields, such as predictive maintenance, customer churn prediction and population lifetime estimation. In this paper, we review and compare the performance of several prediction models for time-to-event analysis. These consist of semi-parametric and parametric statistical models, in addition to machine learning approaches. Our study is carried out on three datasets and evaluated in two different scores (the integrated Brier score and concordance index). Moreover, we show how ensemble methods, which surprisingly have not yet been much studied in time-to-event analysis, can improve the prediction accuracy and enhance the robustness of the prediction performance. We conclude the analysis with a simulation experiment in which we evaluate the factors influencing the performance ranking of the methods using both scores.

Keywords: Ensemble methods; time-to-event analysis; integrated Brier score; concordance index

Domains: Machine Learning [cs.LG]

Camila Fernandez, Chung Shue Chen, Pierre Gaillard, Alonso Silva. Experimental Comparison of Ensemble Methods and Time-to-Event Analysis Models Through Integrated Brier Score and Concordance Index. 2024. ⟨hal-04500089⟩