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Variable screening in predicting clinical outcome with high-dimensional microarrays

Publication ,  Journal Article
Shao, J; Chow, SC
Published in: Journal of Multivariate Analysis
September 1, 2007

Statistical modeling is an important area of biomarker research of important genes for new drug targets, drug candidate validation, disease diagnoses, personalized treatment, and prediction of clinical outcome of a treatment. A widely adopted technology is the use of microarray data that are typically very high dimensional. After screening chromosomes for relative genes using methods such as quantitative trait locus mapping, there may still be a few thousands of genes related to the clinical outcome of interest. On the other hand, the sample size (the number of subjects) in a clinical study is typically much smaller. Under the assumption that only a few important genes are actually related to the clinical outcome, we propose a variable screening procedure to eliminate genes having negligible effects on the clinical outcome. Once the dimension of microarray data is reduced to a manageable number relative to the sample size, one can select a final set of genes via a well-known variable selection method such as the cross-validation. We establish the asymptotic consistency of the proposed variable screening procedure. Some simulation results are also presented. © 2005 Elsevier Inc. All rights reserved.

Duke Scholars

Published In

Journal of Multivariate Analysis

DOI

EISSN

1095-7243

ISSN

0047-259X

Publication Date

September 1, 2007

Volume

98

Issue

8

Start / End Page

1529 / 1538

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Shao, J., & Chow, S. C. (2007). Variable screening in predicting clinical outcome with high-dimensional microarrays. Journal of Multivariate Analysis, 98(8), 1529–1538. https://doi.org/10.1016/j.jmva.2004.12.004
Shao, J., and S. C. Chow. “Variable screening in predicting clinical outcome with high-dimensional microarrays.” Journal of Multivariate Analysis 98, no. 8 (September 1, 2007): 1529–38. https://doi.org/10.1016/j.jmva.2004.12.004.
Shao J, Chow SC. Variable screening in predicting clinical outcome with high-dimensional microarrays. Journal of Multivariate Analysis. 2007 Sep 1;98(8):1529–38.
Shao, J., and S. C. Chow. “Variable screening in predicting clinical outcome with high-dimensional microarrays.” Journal of Multivariate Analysis, vol. 98, no. 8, Sept. 2007, pp. 1529–38. Scopus, doi:10.1016/j.jmva.2004.12.004.
Shao J, Chow SC. Variable screening in predicting clinical outcome with high-dimensional microarrays. Journal of Multivariate Analysis. 2007 Sep 1;98(8):1529–1538.
Journal cover image

Published In

Journal of Multivariate Analysis

DOI

EISSN

1095-7243

ISSN

0047-259X

Publication Date

September 1, 2007

Volume

98

Issue

8

Start / End Page

1529 / 1538

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics