IDEAS home Printed from https://ideas.repec.org/p/bep/mchbio/1030.html
   My bibliography  Save this paper

Covariate adjustment in the analysis of microarray data from clinical studies

Author

Listed:
  • 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

    Download full text from publisher

    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1030&context=umichbiostat
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E. M. Conlon & B. L. Postier & B. A. Methe & K. P. Nevin & D. R. Lovley, 2009. "Hierarchical Bayesian meta-analysis models for cross-platform microarray studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(10), pages 1067-1085.
    2. Izmirlian, Grant, 2020. "Strong consistency and asymptotic normality for quantities related to the Benjamini–Hochberg false discovery rate procedure," Statistics & Probability Letters, Elsevier, vol. 160(C).
    3. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    4. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    5. Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.
    6. Patrick Kline & Christopher Walters, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Papers 1907.06622, arXiv.org, revised Jul 2019.
    7. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    8. Kim, Donggyu & Zhang, Chunming, 2014. "Adaptive linear step-up multiple testing procedure with the bias-reduced estimator," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 31-39.
    9. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Jun 2024.
    10. Debashis Ghosh & Arul Chinnaiyan, 2004. "Semiparametric methods for identification of tumor progression genes from microarray data," The University of Michigan Department of Biostatistics Working Paper Series 1039, Berkeley Electronic Press.
    11. Ahmed Hossain & Hafiz T.A. Khan, 2016. "Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2538-2549, October.
    12. Friguet, Chloé & Causeur, David, 2011. "Estimation of the proportion of true null hypotheses in high-dimensional data under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2665-2676, September.
    13. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    14. HyungJun Cho & Jaewoo Kang & Jae Lee, 2009. "Empirical Bayes analysis of unreplicated microarray data," Computational Statistics, Springer, vol. 24(3), pages 393-408, August.
    15. T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.
    16. Xiaoquan Wen, 2017. "Robust Bayesian FDR Control Using Bayes Factors, with Applications to Multi-tissue eQTL Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 28-49, June.
    17. Patrick Kline & Christopher Walters, 2021. "Reasonable Doubt: Experimental Detection of Job‐Level Employment Discrimination," Econometrica, Econometric Society, vol. 89(2), pages 765-792, March.
    18. Hwang J.T. Gene & Liu Peng, 2010. "Optimal Tests Shrinking Both Means and Variances Applicable to Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-35, October.
    19. Patrick Kline & Evan K Rose & Christopher R Walters, 2022. "Systemic Discrimination Among Large U.S. Employers [“Teachers and Student Achievement in the Chicago Public High Schools,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(4), pages 1963-2036.
    20. Alejandro Ochoa & John D Storey & Manuel Llinás & Mona Singh, 2015. "Beyond the E-Value: Stratified Statistics for Protein Domain Prediction," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-21, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bep:mchbio:1030. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.bepress.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.