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In this paper, we make an experimental comparison of semi-parametric (Coxproportional hazards model, Aalen's additive regression model), parametric(Weibull AFT model), and machine learning models (Random Survival Forest,Gradient Boosting with Cox Proportional Hazards Loss, DeepSurv) through theconcordance index on two different datasets (PBC and GBCSG2). We present twocomparisons: one with the default hyper-parameters of these models and one withthe best hyper-parameters found by randomized search.

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