IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v10y2018i3d10.1007_s12561-018-9216-5.html
   My bibliography  Save this article

Estimation of a Concordance Probability for Doubly Censored Time-to-Event Data

Author

Listed:
  • Kenichi Hayashi

    (Keio University)

  • Yasutaka Shimizu

    (Waseda University)

Abstract

Evaluating the relationship between a response variable and explanatory variables is important to establish better statistical models. Concordance probability is one measure of this relationship and is often used in biomedical research. Concordance probability can be seen as an extension of the area under the receiver operating characteristic curve. In this study, we propose estimators of concordance probability for time-to-event data subject to double censoring. A doubly censored time-to-event response is observed when either left or right censoring may occur. In the presence of double censoring, existing estimators of concordance probability lack desirable properties such as consistency and asymptotic normality. The proposed estimators consist of estimators of the left-censoring and the right-censoring distributions as a weight for each pair of cases, and reduce to the existing estimators in special cases. We show the statistical properties of the proposed estimators and evaluate their performance via numerical experiments.

Suggested Citation

  • Kenichi Hayashi & Yasutaka Shimizu, 2018. "Estimation of a Concordance Probability for Doubly Censored Time-to-Event Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 546-567, December.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:3:d:10.1007_s12561-018-9216-5
    DOI: 10.1007/s12561-018-9216-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-018-9216-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-018-9216-5?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. Shuang Ji & Limin Peng & Yu Cheng & HuiChuan Lai, 2012. "Quantile Regression for Doubly Censored Data," Biometrics, The International Biometric Society, vol. 68(1), pages 101-112, March.
    2. Mithat Gonen & Glenn Heller, 2005. "Concordance probability and discriminatory power in proportional hazards regression," Biometrika, Biometrika Trust, vol. 92(4), pages 965-970, December.
    3. Kim, Yongdai & Kim, Bumsoo & Jang, Woncheol, 2010. "Asymptotic properties of the maximum likelihood estimator for the proportional hazards model with doubly censored data," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1339-1351, July.
    4. Richard Peto, 1973. "Experimental Survival Curves for Interval‐Censored Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 86-91, March.
    5. Lin, Guixian & He, Xuming & Portnoy, Stephen, 2012. "Quantile regression with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 797-812.
    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. Choi, Taehwa & Kim, Arlene K.H. & Choi, Sangbum, 2021. "Semiparametric least-squares regression with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    2. De Silva, Dakshina G. & Kosmopoulou, Georgia & Lamarche, Carlos, 2017. "Subcontracting and the survival of plants in the road construction industry: A panel quantile regression analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 137(C), pages 113-131.
    3. Subramanian, Sundarraman, 2021. "Median regression from twice censored data," Statistics & Probability Letters, Elsevier, vol. 168(C).
    4. Frumento, Paolo & Bottai, Matteo, 2017. "An estimating equation for censored and truncated quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 53-63.
    5. ChunJing Li & Yun Li & Xue Ding & XiaoGang Dong, 2020. "DGQR estimation for interval censored quantile regression with varying-coefficient models," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-17, November.
    6. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    7. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    8. Arazmuradov, Annageldy, 2016. "Assessing sovereign debt default by efficiency," The Journal of Economic Asymmetries, Elsevier, vol. 13(C), pages 100-113.
    9. Nuriye Sancar & Deniz Inan, 2018. "A novel method as a diagnostic tool for the detection of influential observations in the Cox proportional hazards model," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 1253-1266, December.
    10. Liu Xinhua & Jin Zhezhen, 2009. "A Non-Parametric Approach to Scale Reduction for Uni-Dimensional Screening Scales," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-22, January.
    11. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    12. Faustino Prieto & Jos'e Mar'ia Sarabia & Enrique Calder'in-Ojeda, 2020. "The risk of death in newborn businesses during the first years in market," Papers 2011.11776, arXiv.org.
    13. Meei Pyng Ng, 2002. "A Modification of Peto's Nonparametric Estimation of Survival Curves for Interval-Censored Data," Biometrics, The International Biometric Society, vol. 58(2), pages 439-442, June.
    14. Chrianna I Bharat & Kevin Murray & Edward Cripps & Melinda R Hodkiewicz, 2018. "Methods for displaying and calibration of Cox proportional hazards models," Journal of Risk and Reliability, , vol. 232(1), pages 105-115, February.
    15. Jakob W. Messner & Achim Zeileis & Jochen Broecker & Georg J. Mayr, 2013. "Improved Probabilistic Wind Power Forecasts with an Inverse Power Curve Transformation and Censored Regression," Working Papers 2013-01, Faculty of Economics and Statistics, Universität Innsbruck.
    16. Wang, Tong & Jin, Hailong & Sieverding, Heidi & Kumar, Sandeep & Miao, Yuxin & Rao, Xudong & Obembe, Oladipo & Mirzakhani Nafchi, Ali & Redfearn, Daren & Cheye, Stephen, 2023. "Understanding farmer views of precision agriculture profitability in the U.S. Midwest," Ecological Economics, Elsevier, vol. 213(C).
    17. Sean M. Devlin & Mithat Gönen & Glenn Heller, 2020. "Measuring the temporal prognostic utility of a baseline risk score," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 856-871, October.
    18. Yilong Zhang & Xiaoxia Han & Yongzhao Shao, 2021. "The ROC of Cox proportional hazards cure models with application in cancer studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 195-215, April.
    19. Schmid, Matthias & Tutz, Gerhard & Welchowski, Thomas, 2018. "Discrimination measures for discrete time-to-event predictions," Econometrics and Statistics, Elsevier, vol. 7(C), pages 153-164.
    20. Charmaine Pei Ling Lee & Hyungwon Choi & Khee Chee Soo & Min-Han Tan & Wen Yee Chay & Kee Seng Chia & Jenny Liu & Jingmei Li & Mikael Hartman, 2015. "Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-16, September.

    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:spr:stabio:v:10:y:2018:i:3:d:10.1007_s12561-018-9216-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.