IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v63y2001i4p855-870.html
   My bibliography  Save this article

A risk set calibration method for failure time regression by using a covariate reliability sample

Author

Listed:
  • Sharon X. Xie
  • C. Y. Wang
  • Ross L. Prentice

Abstract

Regression parameter estimation in the Cox failure time model is considered when regression variables are subject to measurement error. Assuming that repeat regression vector measurements adhere to a classical measurement model, we can consider an ordinary regression calibration approach in which the unobserved covariates are replaced by an estimate of their conditional expectation given available covariate measurements. However, since the rate of withdrawal from the risk set across the time axis, due to failure or censoring, will typically depend on covariates, we may improve the regression parameter estimator by recalibrating within each risk set. The asymptotic and small sample properties of such a risk set regression calibration estimator are studied. A simple estimator based on a least squares calibration in each risk set appears able to eliminate much of the bias that attends the ordinary regression calibration estimator under extreme measurement error circumstances. Corresponding asymptotic distribution theory is developed, small sample properties are studied using computer simulations and an illustration is provided.

Suggested Citation

  • Sharon X. Xie & C. Y. Wang & Ross L. Prentice, 2001. "A risk set calibration method for failure time regression by using a covariate reliability sample," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 855-870.
    • Handle: RePEc:bla:jorssb:v:63:y:2001:i:4:p:855-870
    DOI: 10.1111/1467-9868.00317
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9868.00317
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9868.00317?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
    ---><---

    Citations

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


    Cited by:

    1. C. Y. Wang, 2008. "Non‐parametric Maximum Likelihood Estimation for Cox Regression with Subject‐Specific Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 613-628, December.
    2. Sehee Kim & Yi Li & Donna Spiegelman, 2016. "A semiparametric copula method for Cox models with covariate measurement error," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 1-16, January.
    3. Ching‐Yun Wang & Xiao Song, 2021. "Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard," Biometrics, The International Biometric Society, vol. 77(2), pages 561-572, June.
    4. Chi-Chung Wen, 2010. "Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(2), pages 199-217, September.
    5. Cheng Zheng & Yingye Zheng, 2019. "Calibrating Variations in Biomarker Measures for Improving Prediction with Time-to-event Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 477-503, December.
    6. Xiaomei Liao & David M. Zucker & Yi Li & Donna Spiegelman, 2011. "Survival Analysis with Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach," Biometrics, The International Biometric Society, vol. 67(1), pages 50-58, March.
    7. Yijian Huang & Ching†Yun Wang, 2018. "Cox regression with dependent error in covariates," Biometrics, The International Biometric Society, vol. 74(1), pages 118-126, March.
    8. Yanlin Tang & Xinyuan Song & Grace Yun Yi, 2022. "Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 139-168, January.
    9. Xiao Song & Edward C. Chao & Ching‐Yun Wang, 2023. "A smoothed corrected score approach for proportional hazards model with misclassified discretized covariates induced by error‐contaminated continuous time‐dependent exposure," Biometrics, The International Biometric Society, vol. 79(1), pages 437-448, March.
    10. Wen Ye & Xihong Lin & Jeremy M. G. Taylor, 2008. "Semiparametric Modeling of Longitudinal Measurements and Time-to-Event Data–A Two-Stage Regression Calibration Approach," Biometrics, The International Biometric Society, vol. 64(4), pages 1238-1246, December.
    11. Pamela A. Shaw & Ross L. Prentice, 2012. "Hazard Ratio Estimation for Biomarker-Calibrated Dietary Exposures," Biometrics, The International Biometric Society, vol. 68(2), pages 397-407, June.
    12. Shanshan Zhao & Ross L. Prentice, 2014. "Covariate measurement error correction methods in mediation analysis with failure time data," Biometrics, The International Biometric Society, vol. 70(4), pages 835-844, December.
    13. Li‐Pang Chen & Grace Y. Yi, 2021. "Analysis of noisy survival data with graphical proportional hazards measurement error models," Biometrics, The International Biometric Society, vol. 77(3), pages 956-969, September.
    14. Li-Pang Chen & Grace Y. Yi, 2021. "Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 481-517, June.

    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:bla:jorssb:v:63:y:2001:i:4:p:855-870. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.