Covariate adjustment in the analysis of microarray data from clinical studies.
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 follow-up validation studies. We develop two approaches to the analysis of microarray data in non-randomized 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 (ROC) curve for the analysis of gene expression data. The ideas are illustrated using data from a prostate cancer molecular profiling study.
Ghosh
Debashis
D
Department of Biostatistics, School of Public Health, University of Michigan, Room M4057, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. ghoshd@umich.edu
Chinnaiyan
Arul M
AM
D020869:Gene Expression Profiling; D006801:Humans; D016014:Linear Models; D009362:Neoplasm Metastasis / Q000235:genetics; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology