JSM 2005 - Toronto

Abstract #303304

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 316
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #303304
Title: How Many Samples are Needed To Build a Classifier? A General Sequential Approach
Author(s): Wenjiang Fu*+ and Edward Dougherty and Bani Mallick and Raymond J. Carroll
Companies: Michigan State University and Texas A&M University and Texas A&M University and Texas A&M University
Address: Dept of Statistics, College Station, TX, 77843-3143, United States
Keywords: Classification ; Clinical diagnosis ; Martingale central limit theorem ; Sample size ; Sequential
Abstract:

Microarray studies have gained increasing popularity. It provides great opportunities to screen thousand of genes simultaneously and posts great challenges with its large-number-of-genes-and-small-sample-size characteristics. As in all quantitative biomedical studies, a key issue in microarray studies is sample size determination. However, conventional methods of sample size calculation may not apply because (1) microarray studies are usually conducted in a novel research area, so data may not be available from previous studies that conventional sample size calculation is based upon; and (2) even with available previous data, new studies may target on different genes and thus variability may vary largely and sample size may not be estimated accurately. Thus, investigators may wish to consider a sequential procedure for recruitment in which there are enough samples to make a sound decision in diagnosis or classification while keeping the sample size as small as possible to make the study affordable. In this paper, we present a general sequential approach and provide an optimal stopping rule.

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Revised March 2005