Add to ORKGIn this paper, we make an experimental comparison of semi-parametric (Cox proportional 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 the concordance index on two different datasets (PBC and GBCSG2). We present two comparisons: one with the default hyper-parameters of these models and one with the best hyper-parameters found by randomized search
An overview of time-to-event parametric methods is presented in this chapter. The parameters hazard ...
The Cox proportional hazard model may predict whether an individual belonging to a given group would...
Survival analysis is an important field of Statistics concerned with mak- ing time-to-event predicti...
Du fait de l'épidémie de CoViD-19, les 52èmes journées de Statistique sont reportées ! Elles auront ...
International audienceIn this paper, we make an experimental comparison of semi-parametric (Cox prop...
Predicting time-to-event from longitudinal data where different events occur at different time point...
The concordance index is often used to measure how well a biomarker predicts the time to an event. E...
Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzi...
Survival analysis focuses on modeling and predicting the time to an event of interest. Many statisti...
The Cox proportional hazards model is the most widely used survival prediction model for analysing t...
In chapter 1 we study explained variation under the additive hazards regression model for right-cens...
The class of semiparametric transformation models provides a very general framework for studying the...
The proportional hazards assumption in the commonly used Cox model for censored failure time data is...
Developing a prognostic model for biomedical applications typically requires mapping an individual's...
Thesis (Ph.D.)--University of Washington, 2023This dissertation develops practical methodology incor...
An overview of time-to-event parametric methods is presented in this chapter. The parameters hazard ...
The Cox proportional hazard model may predict whether an individual belonging to a given group would...
Survival analysis is an important field of Statistics concerned with mak- ing time-to-event predicti...
Du fait de l'épidémie de CoViD-19, les 52èmes journées de Statistique sont reportées ! Elles auront ...
International audienceIn this paper, we make an experimental comparison of semi-parametric (Cox prop...
Predicting time-to-event from longitudinal data where different events occur at different time point...
The concordance index is often used to measure how well a biomarker predicts the time to an event. E...
Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzi...
Survival analysis focuses on modeling and predicting the time to an event of interest. Many statisti...
The Cox proportional hazards model is the most widely used survival prediction model for analysing t...
In chapter 1 we study explained variation under the additive hazards regression model for right-cens...
The class of semiparametric transformation models provides a very general framework for studying the...
The proportional hazards assumption in the commonly used Cox model for censored failure time data is...
Developing a prognostic model for biomedical applications typically requires mapping an individual's...
Thesis (Ph.D.)--University of Washington, 2023This dissertation develops practical methodology incor...
An overview of time-to-event parametric methods is presented in this chapter. The parameters hazard ...
The Cox proportional hazard model may predict whether an individual belonging to a given group would...
Survival analysis is an important field of Statistics concerned with mak- ing time-to-event predicti...