Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Methods for Time-to-Event Analysis Through the IPEC Score - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Methods for Time-to-Event Analysis Through the IPEC Score

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 IPEC score on three different datasets (PBC, GBCSG2 and TLCM).
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Dates and versions

hal-03221512 , version 1 (08-05-2021)

Identifiers

  • HAL Id : hal-03221512 , version 1

Cite

Camila Fernández, Chung Shue Chen, Pierre Gaillard, Alonso Silva. Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Methods for Time-to-Event Analysis Through the IPEC Score. SFdS 2020 - 52èmes Journées de Statistiques de la Société Française de Statistique, Jun 2021, Nice, France. pp.1-6. ⟨hal-03221512⟩
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