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Publication Date:
August 2002
ISSN:
1544-6115
DOI:
10.2202/1544-6115.1000

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Editor-in-Chief: Stumpf, Michael P.H.

Editorial Board Member: Beaumont, Mark / Binder, Harald / Gupta, Mayetri / Hubbard, Alan E. / Husmeier, Dirk / Ji, Hongkai / Keles, Sunduz / Kerr, Kathleen / Lazzeroni, Laura / Lin, Shili / Ma, Ping / Marjoram, Paul / Mertens, Bart / Nerman, Olle / G. Petretto, Enrico / Plagnol, Vincent / Purdom, Elizabeth / Robin, Stéphane / Rzhetsky, Andrey / Sanguinetti, Guido / van der Laan, Mark J. / von Haeseler, Arndt / Weeks, Daniel E. / Wiuf, Carsten / Zhao, Hongyu

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IMPACT FACTOR 2011: 1.517
5-year IMPACT FACTOR: 1.704
Rank 27 out of 116 in category Statistics & Probability in the 2011 Thomson Reuters Journal Citation Report/Science Edition

Pre-validation and inference in microarrays

Robert J. Tibshirani / Brad Efron

1Stanford University

1Stanford University

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 1, Issue 1, Pages –, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1000, August 2002

Publication History:
Published Online:
2002-08-22

In microarray studies, an important problem is to compare a predictor of disease outcome derived from gene expression levels to standard clinical predictors. Comparing them on the same dataset that was used to derive the microarray predictor can lead to results strongly biased in favor of the microarray predictor. We propose a new technique called ``pre-validation'' for making a fairer comparison between the two sets of predictors. We study the method analytically and explore its application in a recent study on breast cancer.

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