IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v6y2007i1n16.html
   My bibliography  Save this article

Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression

Author

Listed:
  • Zhao Qiang

    (Texas State University)

  • Sun Jianguo

    (University of Missouri-Columbia)

Abstract

Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene expression data. For this, several authors have discussed the use of the proportional hazards or Cox model after reducing the dimension of the gene expression data. This paper presents a new approach to conduct the Cox survival analysis of microarray gene expression data with the focus on models' predictive ability. The method modifies the correlation principal component regression (Sun, 1995) to handle the censoring problem of survival data. The results based on simulated data and a set of publicly available data on diffuse large B-cell lymphoma show that the proposed method works well in terms of models' robustness and predictive ability in comparison with some existing partial least squares approaches. Also, the new approach is simpler and easy to implement.

Suggested Citation

  • Zhao Qiang & Sun Jianguo, 2007. "Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-16, May.
    • Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:16
    DOI: 10.2202/1544-6115.1153
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1153
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1153?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nguyen Tuan S & Rojo Javier, 2009. "Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-38, January.

    More about this item

    Statistics

    Access and download statistics

    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:bpj:sagmbi:v:6:y:2007:i:1:n:16. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.