Variable screening in predicting clinical outcome with high-dimensional microarrays

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

Author Shein-Chung Chow Biostatistics & Bioinformatics, Division of Biostatistics