Measures to Summarize and Compare the Predictive Capacity of Markers : The International Journal of Biostatistics

www.degruyter.com uses cookies, tags, and tracking settings to store information that help give you the very best browsing experience.
To understand more about cookies, tags, and tracking, see our Privacy Statement
I accept all cookies for the De Gruyter Online site

Jump to ContentJump to Main Navigation

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.


IMPACT FACTOR 2014: 0.741
5-year IMPACT FACTOR: 1.475

SCImago Journal Rank (SJR) 2014: 1.247
Source Normalized Impact per Paper (SNIP) 2014: 1.078
Impact per Publication (IPP) 2014: 1.206

Mathematical Citation Quotient (MCQ) 2014: 0.07

Measures to Summarize and Compare the Predictive Capacity of Markers

Wen Gu1 / Margaret Pepe2

1Amgen

2University of Washington

Citation Information: The International Journal of Biostatistics. Volume 5, Issue 1, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1188, October 2009

Publication History

Published Online:
2009-10-01

The predictive capacity of a marker in a population can be described using the population distribution of risk (Huang et al. 2007; Pepe et al. 2008a; Stern 2008). Virtually all standard statistical summaries of predictability and discrimination can be derived from it (Gail and Pfeiffer 2005). The goal of this paper is to develop methods for making inference about risk prediction markers using summary measures derived from the risk distribution. We describe some new clinically motivated summary measures and give new interpretations to some existing statistical measures. Methods for estimating these summary measures are described along with distribution theory that facilitates construction of confidence intervals from data. We show how markers and, more generally, how risk prediction models, can be compared using clinically relevant measures of predictability. The methods are illustrated by application to markers of lung function and nutritional status for predicting subsequent onset of major pulmonary infection in children suffering from cystic fibrosis. Simulation studies show that methods for inference are valid for use in practice.

Keywords: discrimination; risk; classification; decision making

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Mario Petretta and Alberto Cuocolo
Current Cardiovascular Imaging Reports, 2016, Volume 9, Number 1
[2]
Giuseppe Casalicchio, Bernd Bischl, Anne-Laure Boulesteix, and Matthias Schmid
Biometrics, 2015, Page n/a
[3]
Mei-Cheng Wang and Shanshan Li
Biometrics, 2012, Volume 68, Number 4, Page 1207
[4]
Kathleen F. Kerr and Margaret S. Pepe
Epidemiology, 2011, Volume 22, Number 6, Page 805
[5]
Vivian Viallon and Aurélien Latouche
Biometrical Journal, 2011, Volume 53, Number 2, Page 217
[6]
Margaret Sullivan Pepe, Kathleen F. Kerr, Gary Longton, and Zheyu Wang
Statistics in Medicine, 2013, Volume 32, Number 9, Page 1467
[7]
Jane Fridlyand, Ru-Fang Yeh, Howard Mackey, Thomas Bengtsson, Paul Delmar, Greg Spaniolo, and Grazyna Lieberman
Contemporary Clinical Trials, 2013, Volume 36, Number 2, Page 624
[8]
Aasthaa Bansal and Margaret Sullivan Pepe
Lifetime Data Analysis, 2013, Volume 19, Number 2, Page 170
[9]
Jens Klotsche, Dietmar Ferger, David Leistner, Lars Pieper, Andreas M. Zeiher, Hans-Ulrich Wittchen, and Juergen Rehm
Biometrical Journal, 2012, Volume 54, Number 6, Page 808
[11]
Nancy R. Cook and Nina P. Paynter
Biometrical Journal, 2011, Volume 53, Number 2, Page 237

Comments (0)

Please log in or register to comment.