Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker

By Patrick J. Heagerty, Thomas Lumley, M. Pepe. Biometrics, 2000

2,631 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4e776e712083bddabc6d2623106dd17f05c31601

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Evaluating Random Forests for Survival Analysis using Prediction Error Curves (2012, methodology): “For event time outcome in survival analysis these measures can be estimated pointwise over time, where pioneering work was done by Graf, Schmoor, Sauerbrei, and Schumacher (1999) for the time-dependent Brier score and by Heagerty, Lumley, and Pepe (2000) for time-dependent ROC analysis.”
Survival Model Predictive Accuracy and ROC Curves (2005, positive): “For a baseline marker value, M i , Heagerty et al. (2000) propose versions of time-dependent sensitivity and specificity using the definitions sensitivity C (c, t) : P (M i Using this approach, at any fixed time t the entire population is classified as either a case or a control on the basis of…”
Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data (2005, methodology): “These methods were recently developed by Heagerty et al. (2000) in the context of the medical diagnosis.”
Explainable AI for Trees: From Local Explanations to Global Understanding (2019, methodology): “Methods 3.1 Mortality dataset For NHANES I mortality prediction in Figure 2A, we used a cox proportional hazards loss, and a C-statistic [24] to measure performance.”
plotROC: A Tool for Plotting ROC Curves (2017, methodology): “Another potential use of this approach is for plotting time-dependent ROC curves for time-to-event outcomes estimated as described in Heagerty, Lumley, and Pepe (2000).”
A targeted real-time early warning score (TREWScore) for septic shock (2015, background): “The conditional inverse probability of censoring weighting (CIPCW) is a standard approach to do so (63, 64).”
Estimation of time‐dependent area under the ROC curve for long‐term risk prediction (2006, background): “[20] have also addressed estimation of ROC curves in the setting of a time-dependent disease variable and as related to one factor Z.”
Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risks (2013, background): “[3, 4] introduced the time-dependent ROC curves and proposed several definitions of cases and controls.”