Skip Navigation


Biostatistics Advance Access originally published online on April 13, 2006
Biostatistics 2007 8(1):101-117; doi:10.1093/biostatistics/kxj036
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
8/1/101    most recent
kxj036v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Dobbin, K. K.
Right arrow Articles by Simon, R. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Dobbin, K. K.
Right arrow Articles by Simon, R. M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Published by Oxford University Press 2006.

Sample size planning for developing classifiers using high-dimensional DNA microarray data

Kevin K. Dobbin* and Richard M. Simon

Biometric Research Branch, National Cancer Institute, 6130 Executive, Boulevard, Rockville, MD 20852, USA dobbinke{at}mail.nih.gov

* To whom correspondence should be addressed.

Many gene expression studies attempt to develop a predictor of pre-defined diagnostic or prognostic classes. If the classes are similar biologically, then the number of genes that are differentially expressed between the classes is likely to be small compared to the total number of genes measured. This motivates a two-step process for predictor development, a subset of differentially expressed genes is selected for use in the predictor and then the predictor constructed from these. Both these steps will introduce variability into the resulting classifier, so both must be incorporated in sample size estimation. We introduce a methodology for sample size determination for prediction in the context of high-dimensional data that captures variability in both steps of predictor development. The methodology is based on a parametric probability model, but permits sample size computations to be carried out in a practical manner without extensive requirements for preliminary data. We find that many prediction problems do not require a large training set of arrays for classifier development.

Keywords: Gene expression; Microarrays; Prediction; Predictive inference; Sample size


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 JCOHome page
J. A. Sparano and S. Paik
Development of the 21-Gene Assay and Its Application in Clinical Practice and Clinical Trials
J. Clin. Oncol., February 10, 2008; 26(5): 721 - 728.
[Abstract] [Full Text] [PDF]

Home page
 Clin. Cancer Res.Home page
K. K. Dobbin, Y. Zhao, and R. M. Simon
How Large a Training Set is Needed to Develop a Classifier for Microarray Data?
Clin. Cancer Res., January 1, 2008; 14(1): 108 - 114.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.