Add to ORKGInternational audienceIn 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)
Non-parametric survival analysis techniques are often used in clinical and epidemiologic research to...
Although semi- and non-parametric approaches are frequently used to analyse survival data, there are...
An overview of time-to-event parametric methods is presented in this chapter. The parameters hazard ...
International audienceIn this paper, we make an experimental comparison of semi-parametric (Cox prop...
Du fait de l'épidémie de CoViD-19, les 52èmes journées de Statistique sont reportées ! Elles auront ...
Machine Learning Models are known to understand the intricacies of the data well, but native ML mode...
The Cox proportional hazard model may predict whether an individual belonging to a given group would...
Predicting time-to-event from longitudinal data where different events occur at different time point...
Survival analysis is an important field of Statistics concerned with mak- ing time-to-event predicti...
Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzi...
Thesis (Ph.D.)--University of Washington, 2023This dissertation develops practical methodology incor...
In chapter 1 we study explained variation under the additive hazards regression model for right-cens...
Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, med...
The Cox Proportional hazard model is a popular method to analyze right-censored survival data. This ...
The aim of this study was to make a comparison among existing estimation methods (Kaplan-Meier, Nels...
Non-parametric survival analysis techniques are often used in clinical and epidemiologic research to...
Although semi- and non-parametric approaches are frequently used to analyse survival data, there are...
An overview of time-to-event parametric methods is presented in this chapter. The parameters hazard ...
International audienceIn this paper, we make an experimental comparison of semi-parametric (Cox prop...
Du fait de l'épidémie de CoViD-19, les 52èmes journées de Statistique sont reportées ! Elles auront ...
Machine Learning Models are known to understand the intricacies of the data well, but native ML mode...
The Cox proportional hazard model may predict whether an individual belonging to a given group would...
Predicting time-to-event from longitudinal data where different events occur at different time point...
Survival analysis is an important field of Statistics concerned with mak- ing time-to-event predicti...
Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzi...
Thesis (Ph.D.)--University of Washington, 2023This dissertation develops practical methodology incor...
In chapter 1 we study explained variation under the additive hazards regression model for right-cens...
Survival analysis (time-to-event analysis) is widely used in economics and finance, engineering, med...
The Cox Proportional hazard model is a popular method to analyze right-censored survival data. This ...
The aim of this study was to make a comparison among existing estimation methods (Kaplan-Meier, Nels...
Non-parametric survival analysis techniques are often used in clinical and epidemiologic research to...
Although semi- and non-parametric approaches are frequently used to analyse survival data, there are...
An overview of time-to-event parametric methods is presented in this chapter. The parameters hazard ...