Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2020

Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index

Abstract

In this paper, we make an experimental comparison of semi-parametric (Cox proportional hazards model, Aalen additive model), parametric (Weibull AFT model), and machine learning methods (Random Survival Forest, Gradient Boosting Cox proportional hazards loss, DeepSurv) through the concordance index on two different datasets (PBC and GBCSG2). We present two comparisons: one with the default hyperparameters of these methods and one with the best hyperparameters found by randomized search.
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Dates and versions

hal-02894974 , version 1 (09-07-2020)

Identifiers

  • HAL Id : hal-02894974 , version 1

Cite

Camila Fernandez, Chung Shue Chen, Pierre Gaillard, Alonso Silva. Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index. JDS 2020 - 52nd Statistics Days of the French Statistical Society (SFdS), May 2020, Nice, France. ⟨hal-02894974⟩
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