To derive models suitable for outcome prediction, a crucial aspect is the availability of appropriate measures of predictive accuracy, which have to be usable for a general class of models. The Harrell's C discrimination index is an extension of the area under the ROC curve to the case of censored survival data, which owns a straightforward interpretability. For a model including covariates with time-dependent effects and/or time-dependent covariates, the original definition of C would require the prediction of individual failure times, which is not generally addressed in most clinical applications. Here we propose a time-dependent discrimination index Ctd where the whole predicted survival function is utilized as outcome prediction, and th...
The area under the receiver operating characteristic curve is often used as a summary index of the d...
International audienceSeveral studies for the clinical validity of circulating tumor cells (CTCs) in...
The aim of this paper is to design a learning machine for the predictive modeling of independently r...
Discrimination statistics describe the ability of a survival model to assign higher risks to individ...
The Cox proportional hazards model is the most widely used survival prediction model for analysing t...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation we develop new methods for quanti...
MotivationDiscrimination statistics describe the ability of a survival model to assign higher risks ...
International audienceFinding out biomarkers and building risk scores to predict the occurrence of s...
SUMMARY. ROC curves are a popular method for displaying sensitivity and specificity of a continuous ...
The predictive accuracy of a survival model can be summarized using extensions of the proportion of ...
Developing a prognostic model for biomedical applications typically requires mapping an individual's...
Background. Risk prediction models can be used as an aid when determining patient management. Becaus...
Clinical prognostic models use information about a patient's characteristics and medical history to ...
Survival analysis is a popular area of statistics dealing with time-to-event data. A special charact...
Although the area under the receiver operating characteristic (AUC) is the most popular measure of t...
The area under the receiver operating characteristic curve is often used as a summary index of the d...
International audienceSeveral studies for the clinical validity of circulating tumor cells (CTCs) in...
The aim of this paper is to design a learning machine for the predictive modeling of independently r...
Discrimination statistics describe the ability of a survival model to assign higher risks to individ...
The Cox proportional hazards model is the most widely used survival prediction model for analysing t...
Thesis (Ph.D.)--University of Washington, 2015In this dissertation we develop new methods for quanti...
MotivationDiscrimination statistics describe the ability of a survival model to assign higher risks ...
International audienceFinding out biomarkers and building risk scores to predict the occurrence of s...
SUMMARY. ROC curves are a popular method for displaying sensitivity and specificity of a continuous ...
The predictive accuracy of a survival model can be summarized using extensions of the proportion of ...
Developing a prognostic model for biomedical applications typically requires mapping an individual's...
Background. Risk prediction models can be used as an aid when determining patient management. Becaus...
Clinical prognostic models use information about a patient's characteristics and medical history to ...
Survival analysis is a popular area of statistics dealing with time-to-event data. A special charact...
Although the area under the receiver operating characteristic (AUC) is the most popular measure of t...
The area under the receiver operating characteristic curve is often used as a summary index of the d...
International audienceSeveral studies for the clinical validity of circulating tumor cells (CTCs) in...
The aim of this paper is to design a learning machine for the predictive modeling of independently r...