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Covariate adjustment in the analysis of microarray data from clinical studies

Author

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  • 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.

Suggested Citation

  • Debashis Ghosh & Arul Chinnaiyan, 2004. "Covariate adjustment in the analysis of microarray data from clinical studies," The University of Michigan Department of Biostatistics Working Paper Series 1030, Berkeley Electronic Press.
    • Handle: RePEc:bep:mchbio:1030
    Note: oai:bepress.com:umichbiostat-1030
    as

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    References listed on IDEAS

    as
    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.
    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.
    3. Saravana M. Dhanasekaran & Terrence R. Barrette & Debashis Ghosh & Rajal Shah & Sooryanarayana Varambally & Kotoku Kurachi & Kenneth J. Pienta & Mark A. Rubin & Arul M. Chinnaiyan, 2001. "Delineation of prognostic biomarkers in prostate cancer," Nature, Nature, vol. 412(6849), pages 822-826, August.
    4. Sooryanarayana Varambally & Saravana M. Dhanasekaran & Ming Zhou & Terrence R. Barrette & Chandan Kumar-Sinha & Martin G. Sanda & Debashis Ghosh & Kenneth J. Pienta & Richard G. A. B. Sewalt & Arie P., 2002. "The polycomb group protein EZH2 is involved in progression of prostate cancer," Nature, Nature, vol. 419(6907), pages 624-629, October.
    5. Margaret Sullivan Pepe & Gary Longton & Garnet L. Anderson & Michel Schummer, 2003. "Selecting Differentially Expressed Genes from Microarray Experiments," Biometrics, The International Biometric Society, vol. 59(1), pages 133-142, March.
    6. 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, August.
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