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Covariate adjustment in the analysis of microarray data from clinical studies

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Author Info
Debashis Ghosh (University of Michigan)
Arul Chinnaiyan (University of Michigan Pathology/Urology)
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.

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File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1030&context=umichbiostat
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Publisher Info
Paper provided by Berkeley Electronic Press in its series The University of Michigan Department of Biostatistics Working Paper Series with number 1030.

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Date of creation: 11 Jul 2004
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Handle: RePEc:bep:mchbio:1030

Note: oai:bepress.com:umichbiostat-1030
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Related research
Keywords: differential expression; gene expression; multiple comparisons; simultaneous inference;

References listed on IDEAS
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  1. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March. [Downloadable!] (restricted)
  2. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December. [Downloadable!] (restricted)
  3. John D. Storey, 2002. "A direct approach to false discovery rates," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498. [Downloadable!] (restricted)
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