IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v12y2016i2p18n11.html
   My bibliography  Save this article

Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring

Author

Listed:
  • Yu Hsiang

    (Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan)

  • Cheng Yu-Jen

    (Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan)

  • Wang Ching-Yun

    (Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N PO Box 19024 M2B500, Seattle, WA 98109, USA)

Abstract

Recurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections of HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out. Further, some covariates may be measured with errors. To accommodate for both informative censoring and measurement error, the occurrence of recurrent events is modelled through an unspecified frailty distribution and accompanied with a classical measurement error model. We propose two corrected approaches based on different ideas, and we show that they are numerically identical when estimating the regression parameters. The asymptotic properties of the proposed estimators are established, and the finite sample performance is examined via simulations. The proposed methods are applied to the Nutritional Prevention of Cancer trial for assessing the effect of the plasma selenium treatment on the recurrence of squamous cell carcinoma.

Suggested Citation

  • Yu Hsiang & Cheng Yu-Jen & Wang Ching-Yun, 2016. "Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring," The International Journal of Biostatistics, De Gruyter, vol. 12(2), pages 1-18, November.
  • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:2:p:18:n:11
    DOI: 10.1515/ijb-2016-0001
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/ijb-2016-0001
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/ijb-2016-0001?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. Wang, C. Y., 1999. "Robust sandwich covariance estimation for regression calibration estimator in Cox regression with measurement error," Statistics & Probability Letters, Elsevier, vol. 45(4), pages 371-378, December.
    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. C. Y. Wang & Naisyin Wang & Suojin Wang, 2000. "Regression Analysis When Covariates Are Regression Parameters of a Random Effects Model for Observed Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 56(2), pages 487-495, June.
    2. Ching-Yun Wang & Harry Cullings & Xiao Song & Kenneth J. Kopecky, 2017. "Joint non-parametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1583-1599, November.
    3. 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.
    4. 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.
    5. Liā€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.

    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:bpj:ijbist:v:12:y:2016:i:2:p:18:n:11. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.