Study design in high-dimensional classification analysis. (5th May 2016)

• Record Type: Journal Article
• Authors: Sánchez, Brisa N.; Wu, Meihua; Song, Peter X. K.; Wang, Wen
• Is Part Of: Biostatistics. Volume 17:Number 4(2016:Oct.)
• Page Start: 722
• Page End: 736
• Publication Date: 2016-05-05
• Subjects: Design -- Higher criticism threshold -- Large p small n -- Linear discrimination -- Sample size; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxw018
• Languages: English
• ISSNs: 1465-4644

Abstract

Abstract : Advances in high throughput technology have accelerated the use of hundreds to millions of biomarkers to construct classifiers that partition patients into different clinical conditions. Prior to classifier development in actual studies, a critical need is to determine the sample size required to reach a specified classification precision. We develop a systematic approach for sample size determination in high-dimensional (large $p$ small $n$ ) classification analysis. Our method utilizes the probability of correct classification (PCC) as the optimization objective function and incorporates the higher criticism thresholding procedure for classifier development. Further, we derive the theoretical bound of maximal PCC gain from feature augmentation (e.g. when molecular and clinical predictors are combined in classifier development). Our methods are motivated and illustrated by a study using proteomics markers to classify post-kidney transplantation patients into stable and rejecting classes.