Estimation of time‐dependent area under the ROC curve for long‐term risk prediction

By L. Chambless, G. Diao. Statistics in Medicine, 2006

319 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Epidemiology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b7ca811c3a39fc2cefbca50a0555707835ad6e6a

How researchers see this paper

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

In Endocrinology, Diabetes and Metabolism

1. Jay P. Singh
2. Ewout W. Steyerberg
3. Michael J. Pencina
4. Ramachandran S. Vasan
5. Andrew J. Vickers
6. Michael W. Kattan
7. Nancy A. Obuchowski
8. Mithat Gonen

In Epidemiology

1. Jay P. Singh
2. Michael J. Pencina
3. Ewout W. Steyerberg
4. Nancy R. Cook
5. Andrew J. Vickers
6. Michael W. Kattan
7. Mithat Gonen
8. Thomas Alexander Gerds

In Statistics and Probability

1. Ewout W. Steyerberg
2. Andrew J. Vickers
3. Michael J. Pencina
4. Jay P. Singh
5. Ramachandran S. Vasan
6. Nancy R. Cook
7. Ralph B. D' Agostino
8. Thomas Alexander Gerds

Citations in the corpus

• Predictive validity performance indicators in violence risk assessment: a methodological primer. (2013, background): “Alternative models have been recently proposed to take stochastic information into account (Chambless & Diao, 2006; Heagerty & Zheng, 2005), though these have not made their way into the mainstream yet.”
• Assessing the performance of prediction models: a framework for some traditional and novel measures (2010, methodology): “Furthermore, time-dependent c statistics have been proposed.(6,38) In addition to the c statistic, the discrimination slope can be used as a simple measure for how well subjects with and without the outcome are separated.”
• Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond (2008, background): “Chambless and Diao [ 34 ] show that ignoring time to event in studies with long follow-up might lead to biased estimates of the AUC, sensitivity and specificity, and suggest ways to rectify the problem.”
• Time-dependent ROC curve analysis in medical research: current methods and applications (2017, methodology): “(CD3) Kaplan-Meier like estimator of Chambless and Diao [4]”
• Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk in the Atherosclerosis Risk in Communities (ARIC) study (2010, unknown): “We described the area under the receiver operator characteristic curve (AUC) for 10-year risk using methods which accounted for censoring (20) for each of the models to describe the model predictivity.”