Covariate adjustment in the analysis of microarray data from clinical studies

Debashis Ghosh and Arul Chinnaiyan
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Debashis Ghosh: University of Michigan
Arul Chinnaiyan: University of Michigan Pathology/Urology

No 1030, The University of Michigan Department of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: There is tremendous scientific interest in the analysis of gene expression data in clinical settings, such as oncology. In this paper, we describe the importance of adjusting for confounders and other prognostic factors in order to select for differentially expressed genes for followup validation studies. We develop two approaches to the analysis of microarray data in nonrandomized clinical settings. The first is an extension of the current significance analysis of microarray procedures, where other covariates are taken into account. The second is a novel covariate-adjusted regression modelling based on the receiver operating characteristic curve for the analysis of gene expression data. The ideas are illustrated using data from a prostate cancer molecular profiling study.

Keywords: differential expression; gene expression; multiple comparisons; simultaneous inference (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:umichbiostat-1030
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