IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v61y2005i3p702-714.html
   My bibliography  Save this article

On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error

Author

Listed:
  • Xiao Song
  • Yijian Huang

Abstract

No abstract is available for this item.

Suggested Citation

  • Xiao Song & Yijian Huang, 2005. "On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error," Biometrics, The International Biometric Society, vol. 61(3), pages 702-714, September.
    • Handle: RePEc:bla:biomet:v:61:y:2005:i:3:p:702-714
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2005.00349.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Xiao Song & Yijian Huang, 2004. "On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error," UW Biostatistics Working Paper Series 1058, Berkeley Electronic Press.
    2. Xiao Song & Yijian Huang, 2004. "A Corrected Pseudo-score Approach for Additive Hazards Model With Longitudinal Covariates Measured With Error," UW Biostatistics Working Paper Series 1049, Berkeley Electronic Press.
    3. Xiao Song & Marie Davidian & Anastasios A. Tsiatis, 2002. "A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 58(4), pages 742-753, December.
    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. Ying Yan & Grace Y. Yi, 2016. "Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 321-342, July.
    2. Yanqing Sun & Qingning Zhou & Peter B. Gilbert, 2023. "Analysis of the Cox Model with Longitudinal Covariates with Measurement Errors and Partly Interval Censored Failure Times, with Application to an AIDS Clinical Trial," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 430-454, July.
    3. Xiao Song & C. Y. Wang, 2008. "Semiparametric Approaches for Joint Modeling of Longitudinal and Survival Data with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 64(2), pages 557-566, June.
    4. Ying Yan & Grace Y. Yi, 2016. "A Class of Functional Methods for Error-Contaminated Survival Data Under Additive Hazards Models with Replicate Measurements," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 684-695, April.
    5. Cheng Zheng & Yiwen Zhang & Ying Huang & Ross Prentice, 2023. "Using Controlled Feeding Study for Biomarker Development in Regression Calibration for Disease Association Estimation," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 57-113, April.
    6. Sihai Dave Zhao & Yi Li, 2014. "Score test variable screening," Biometrics, The International Biometric Society, vol. 70(4), pages 862-871, December.
    7. Yih-Huei Huang & Chi-Chung Wen & Yu-Hua Hsu, 2015. "The Extensively Corrected Score for Measurement Error Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 911-924, December.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Yuhang Xu & Yehua Li & Xiao Song, 2016. "Locally Efficient Semiparametric Estimators for Proportional Hazards Models with Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 558-572, June.
    13. Zhaozhi Fan & Xiao-Feng Wang, 2009. "Marginal hazards model for multivariate failure time data with auxiliary covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 771-786.

    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. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    2. Roula Tsonaka & Geert Verbeke & Emmanuel Lesaffre, 2009. "A Semi-Parametric Shared Parameter Model to Handle Nonmonotone Nonignorable Missingness," Biometrics, The International Biometric Society, vol. 65(1), pages 81-87, March.
    3. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    4. Yih‐Huei Huang & Wen‐Han Hwang & Fei‐Yin Chen, 2016. "Improving efficiency using the Rao–Blackwell theorem in corrected and conditional score estimation methods for joint models," Biometrics, The International Biometric Society, vol. 72(4), pages 1136-1144, December.
    5. Samuel I. Watson & Richard J. Lilford & Jianxia Sun & Julian Bion, 2021. "Estimating the effect of health service delivery interventions on patient length of stay: A Bayesian survival analysis approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1164-1186, November.
    6. Wei Yang & Dawei Xie & Qiang Pan & Harold I. Feldman & Wensheng Guo, 2017. "Joint Modeling of Repeated Measures and Competing Failure Events in a Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 504-524, December.
    7. Jeevanantham Rajeswaran & Eugene H Blackstone & John Barnard, 2018. "Joint Modeling of Multivariate Longitudinal Data and Competing Risks Using Multiphase Sub-models," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 651-685, December.
    8. Erning Li & Naisyin Wang & Nae-Yuh Wang, 2007. "Joint Models for a Primary Endpoint and Multiple Longitudinal Covariate Processes," Biometrics, The International Biometric Society, vol. 63(4), pages 1068-1078, December.
    9. Hongtu Zhu & Joseph G. Ibrahim & Yueh-Yun Chi & Niansheng Tang, 2012. "Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 954-964, September.
    10. repec:jss:jstsof:35:i09 is not listed on IDEAS
    11. Jiin Choi & Stewart J. Anderson & Thomas J. Richards & Wesley K. Thompson, 2014. "Prediction of transplant-free survival in idiopathic pulmonary fibrosis patients using joint models for event times and mixed multivariate longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(10), pages 2192-2205, October.
    12. Xiao Song & C. Y. Wang, 2008. "Semiparametric Approaches for Joint Modeling of Longitudinal and Survival Data with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 64(2), pages 557-566, June.
    13. Scolas, Sylvie & El Ghouch, Anouar & Legrand, Catherine, 2016. "The SNP representation in mixture cure models with interval-censoring: estimation and goodness-of-fit testing," LIDAM Discussion Papers ISBA 2016049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Fushing Hsieh & Yi-Kuan Tseng & Jane-Ling Wang, 2006. "Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited," Biometrics, The International Biometric Society, vol. 62(4), pages 1037-1043, December.
    15. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    16. Paul S. Albert & Joanna H. Shih, 2010. "On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure," Biometrics, The International Biometric Society, vol. 66(3), pages 983-987, September.
    17. Melkamu Molla Ferede & Samuel Mwalili & Getachew Dagne & Simon Karanja & Workagegnehu Hailu & Mahmoud El-Morshedy & Afrah Al-Bossly, 2022. "A Semiparametric Bayesian Joint Modelling of Skewed Longitudinal and Competing Risks Failure Time Data: With Application to Chronic Kidney Disease," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
    18. Xianzheng Huang & Leonard A. Stefanski & Marie Davidian, 2009. "Latent-Model Robustness in Joint Models for a Primary Endpoint and a Longitudinal Process," Biometrics, The International Biometric Society, vol. 65(3), pages 719-727, September.
    19. Qi Gong & Douglas E. Schaubel, 2013. "Partly Conditional Estimation of the Effect of a Time-Dependent Factor in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 69(2), pages 338-347, June.
    20. Elizabeth R. Brown & Joseph G. Ibrahim & Victor DeGruttola, 2005. "A Flexible B-Spline Model for Multiple Longitudinal Biomarkers and Survival," Biometrics, The International Biometric Society, vol. 61(1), pages 64-73, March.
    21. Jaeun Choi & Jianwen Cai & Donglin Zeng, 2017. "Penalized Likelihood Approach for Simultaneous Analysis of Survival Time and Binary Longitudinal Outcome," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 190-216, November.

    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:biomet:v:61:y:2005:i:3:p:702-714. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.