IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0047627.html
   My bibliography  Save this article

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Author

Listed:
  • Takeshi Emura
  • Yi-Hau Chen
  • Hsuan-Yu Chen

Abstract

Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package “compound.Cox” available in CRAN at http://cran.r-project.org/.

Suggested Citation

  • Takeshi Emura & Yi-Hau Chen & Hsuan-Yu Chen, 2012. "Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-12, October.
    • Handle: RePEc:plo:pone00:0047627
    DOI: 10.1371/journal.pone.0047627
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0047627
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0047627&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0047627?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
    ---><---

    References listed on IDEAS

    as
    1. Xi Zhao & Einar Andreas Rødland & Therese Sørlie & Bjørn Naume & Anita Langerød & Arnoldo Frigessi & Vessela N Kristensen & Anne-Lise Børresen-Dale & Ole Christian Lingjærde, 2011. "Combining Gene Signatures Improves Prediction of Breast Cancer Survival," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Ahmed A. Ewees & Mohammed A. A. Al-qaness & Laith Abualigah & Diego Oliva & Zakariya Yahya Algamal & Ahmed M. Anter & Rehab Ali Ibrahim & Rania M. Ghoniem & Mohamed Abd Elaziz, 2021. "Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    2. Emura, Takeshi & Chen, Yi-Hau, 2014. "Gene selection for survival data under dependent censoring: a copula-based approach," MPRA Paper 58043, University Library of Munich, Germany.
    3. Emura, Takeshi & Kao, Fan-Hsuan & Michimae, Hirofumi, 2014. "An improved nonparametric estimator of sub-distribution function for bivariate competing risk models," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 229-241.
    4. Jialiang Li & Tonghui Yu & Jing Lv & Mei‐Ling Ting Lee, 2021. "Semiparametric model averaging prediction for lifetime data via hazards regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1187-1209, November.

    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. Emura, Takeshi & Chen, Yi-Hau & Chen, Hsuan-Yu, 2012. "Survival prediction based on compound covariate under cox proportional hazard models," MPRA Paper 41149, University Library of Munich, Germany.

    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:plo:pone00:0047627. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.