IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v82y2015icp186-198.html
   My bibliography  Save this article

Empirical likelihood ratio confidence interval estimation of best linear combinations of biomarkers

Author

Listed:
  • Chen, Xiwei
  • Vexler, Albert
  • Markatou, Marianthi

Abstract

A novel smoothed empirical likelihood (EL) approach that incorporates kernel estimation of the area under the receiver operating characteristic curve (AUC) to construct nonparametric confidence intervals of AUC based on the best linear combination (BLC) of biomarkers is proposed. The method has several advantages including the feasibility to use gradient-based techniques for fast computation of BLC coefficients and to employ powerful likelihood methods without specification of underlying data distributions. Simulation results show that the new method performs well even when the distribution of biomarkers is skewed, a situation commonly met in practice. A data set from a clinical experiment related to atherosclerotic coronary heart disease is used to illustrate the efficiency of the proposed method.

Suggested Citation

  • Chen, Xiwei & Vexler, Albert & Markatou, Marianthi, 2015. "Empirical likelihood ratio confidence interval estimation of best linear combinations of biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 186-198.
    • Handle: RePEc:eee:csdana:v:82:y:2015:i:c:p:186-198
    DOI: 10.1016/j.csda.2014.09.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947314002710
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2014.09.010?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.

    References listed on IDEAS

    as
    1. Margaret Sullivan Pepe & Tianxi Cai & Gary Longton, 2006. "Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve," Biometrics, The International Biometric Society, vol. 62(1), pages 221-229, March.
    2. Lloyd, Chris J. & Yong, Zhou, 1999. "Kernel estimators of the ROC curve are better than empirical," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 221-228, September.
    3. Gengsheng Qin & Xiao-Hua Zhou, 2006. "Empirical Likelihood Inference for the Area under the ROC Curve," Biometrics, The International Biometric Society, vol. 62(2), pages 613-622, June.
    4. Shuangge Ma & Jian Huang, 2007. "Combining Multiple Markers for Classification Using ROC," Biometrics, The International Biometric Society, vol. 63(3), pages 751-757, September.
    5. Martin W. McIntosh & Margaret Sullivan Pepe, 2002. "Combining Several Screening Tests: Optimality of the Risk Score," Biometrics, The International Biometric Society, vol. 58(3), pages 657-664, September.
    6. Margaret Sullivan Pepe & Tianxi Cai, 2004. "The Analysis of Placement Values for Evaluating Discriminatory Measures," Biometrics, The International Biometric Society, vol. 60(2), pages 528-535, June.
    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. Weining Shen & Jing Ning & Ying Yuan & Anna S. Lok & Ziding Feng, 2018. "Model†free scoring system for risk prediction with application to hepatocellular carcinoma study," Biometrics, The International Biometric Society, vol. 74(1), pages 239-248, March.

    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. Chiang, Chin-Tsang & Chiu, Chih-Heng, 2012. "Nonparametric and semiparametric optimal transformations of markers," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 124-141, January.
    2. Osamu Komori, 2011. "A boosting method for maximization of the area under the ROC curve," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(5), pages 961-979, October.
    3. Zhang Zhiwei & Ma Shujie & Nie Lei & Soon Guoxing, 2017. "A Quantitative Concordance Measure for Comparing and Combining Treatment Selection Markers," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-24, May.
    4. Yanqing Wang & Ying‐Qi Zhao & Yingye Zheng, 2020. "Learning‐based biomarker‐assisted rules for optimized clinical benefit under a risk constraint," Biometrics, The International Biometric Society, vol. 76(3), pages 853-862, September.
    5. Xin Huang & Gengsheng Qin & Yixin Fang, 2011. "Optimal Combinations of Diagnostic Tests Based on AUC," Biometrics, The International Biometric Society, vol. 67(2), pages 568-576, June.
    6. Elisa–María Molanes-López & Ricardo Cao, 2008. "Relative density estimation for left truncated and right censored data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(8), pages 693-720.
    7. Carol Y. Lin & Lance A. Waller & Robert H. Lyles, 2012. "The likelihood approach for the comparison of medical diagnostic system with multiple binary tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1437-1454, December.
    8. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    9. Yuanjia Wang & Huaihou Chen & Runze Li & Naihua Duan & Roberto Lewis-Fernández, 2011. "Prediction-Based Structured Variable Selection through the Receiver Operating Characteristic Curves," Biometrics, The International Biometric Society, vol. 67(3), pages 896-905, September.
    10. Sonia Pérez-Fernández & Pablo Martínez-Camblor & Peter Filzmoser & Norberto Corral, 2021. "Visualizing the decision rules behind the ROC curves: understanding the classification process," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 135-161, March.
    11. Soutik Ghosal & Zhen Chen, 2022. "Discriminatory Capacity of Prenatal Ultrasound Measures for Large-for-Gestational-Age Birth: A Bayesian Approach to ROC Analysis Using Placement Values," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 1-22, April.
    12. Holly Janes & Gary Longton & Margaret S. Pepe, 2009. "Accommodating covariates in receiver operating characteristic analysis," Stata Journal, StataCorp LP, vol. 9(1), pages 17-39, March.
    13. Binbing Yu, 2009. "Approximating the risk score for disease diagnosis using MARS," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(7), pages 769-778.
    14. Qing Lu & Nancy Obuchowski & Sungho Won & Xiaofeng Zhu & Robert C. Elston, 2010. "Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic Tests," Biometrics, The International Biometric Society, vol. 66(2), pages 586-593, June.
    15. Weining Shen & Jing Ning & Ying Yuan & Anna S. Lok & Ziding Feng, 2018. "Model†free scoring system for risk prediction with application to hepatocellular carcinoma study," Biometrics, The International Biometric Society, vol. 74(1), pages 239-248, March.
    16. Yuxin Zhu & Mei‐Cheng Wang, 2022. "Obtaining optimal cutoff values for tree classifiers using multiple biomarkers," Biometrics, The International Biometric Society, vol. 78(1), pages 128-140, March.
    17. Wang, Suohong & Zhang, Biao, 2014. "Semiparametric empirical likelihood confidence intervals for AUC under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 101-115.
    18. Rocío Aznar-Gimeno & Luis M. Esteban & Gerardo Sanz & Rafael del-Hoyo-Alonso & Ricardo Savirón-Cornudella, 2021. "Incorporating a New Summary Statistic into the Min–Max Approach: A Min–Max–Median, Min–Max–IQR Combination of Biomarkers for Maximising the Youden Index," Mathematics, MDPI, vol. 9(19), pages 1-17, October.
    19. Heikki Kauppi, 2016. "The Generalized Receiver Operating Characteristic Curve," Discussion Papers 114, Aboa Centre for Economics.
    20. Pablo Gonzalez Ginestet & Ales Kotalik & David M. Vock & Julian Wolfson & Erin E. Gabriel, 2021. "Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV Register," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 51-65, January.

    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:eee:csdana:v:82:y:2015:i:c:p:186-198. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.