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Biostatistics Advance Access published online on April 13, 2006
Biostatistics, doi:10.1093/biostatistics/kxj036
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Published by Oxford University Press 2006.
Received October 21, 2005
Revised March 31, 2006
Accepted April 7, 2006

Article

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

Kevin K. Dobbin 1 * and Richard M. Simon 1

1 Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD

* To whom correspondence should be addressed.
Kevin K. Dobbin, E-mail: dobbinke{at}mail.nih.gov


  Abstract

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 of 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: prediction; predictive inference; sample size; microarrays; gene expression.
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