IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i23p3667-d1527392.html
   My bibliography  Save this article

Semiparametric Analysis of Additive–Multiplicative Hazards Model with Interval-Censored Data and Panel Count Data

Author

Listed:
  • Tong Wang

    (School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China)

  • Yang Li

    (School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China)

  • Jianguo Sun

    (Department of Statistics, University of Missouri, Columbia, MO 65211, USA)

  • Shuying Wang

    (School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China)

Abstract

In survival analysis, interval-censored data and panel count data represent two prevalent types of incomplete data. Given that, within certain research contexts, the events of interest may simultaneously involve both data types, it is imperative to perform a joint analysis of these data to fully comprehend the occurrence process of the events being studied. In this paper, a novel semiparametric joint regression analysis framework is proposed for the analysis of interval-censored data and panel count data. It is hypothesized that the failure time follows an additive–multiplicative hazards model, while the recurrent events follow a nonhomogeneous Poisson process. Additionally, a gamma-distributed frailty is introduced to describe the correlation between the failure time and the count process of recurrent events. To estimate the model parameters, a sieve maximum likelihood estimation method based on Bernstein polynomials is proposed. The performance of this estimation method under finite sample conditions is evaluated through a series of simulation studies, and an empirical study is illustrated.

Suggested Citation

  • Tong Wang & Yang Li & Jianguo Sun & Shuying Wang, 2024. "Semiparametric Analysis of Additive–Multiplicative Hazards Model with Interval-Censored Data and Panel Count Data," Mathematics, MDPI, vol. 12(23), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3667-:d:1527392
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/23/3667/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/23/3667/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Da Xu & Hui Zhao & Jianguo Sun, 2018. "Joint analysis of interval-censored failure time data and panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 94-109, January.
    2. Peijie Wang & Hui Zhao & Jianguo Sun, 2016. "Regression analysis of case K interval‐censored failure time data in the presence of informative censoring," Biometrics, The International Biometric Society, vol. 72(4), pages 1103-1112, December.
    3. Yao, Bin & Wang, Lianming & He, Xin, 2016. "Semiparametric regression analysis of panel count data allowing for within-subject correlation," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 47-59.
    4. Chi‐Chung Wen & Yi‐Hau Chen, 2018. "Pseudo and conditional score approach to joint analysis of current count and current status data," Biometrics, The International Biometric Society, vol. 74(4), pages 1223-1231, December.
    5. Wen, Chi-Chung & Chen, Yi-Hau, 2016. "Joint analysis of current count and current status data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 153-164.
    6. X. Joan Hu & Stephen W. Lagakos & Richard A. Lockhart, 2009. "Marginal analysis of panel counts through estimating functions," Biometrika, Biometrika Trust, vol. 96(2), pages 445-456.
    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. Du, Mingyue & Zhao, Xingqiu, 2024. "A conditional approach for regression analysis of case K interval-censored failure time data with informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
    2. Gang Cheng & Ying Zhang & Liqiang Lu, 2011. "Efficient algorithms for computing the non and semi-parametric maximum likelihood estimates with panel count data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 567-579.
    3. Rong Liu & Shishun Zhao & Tao Hu & Jianguo Sun, 2022. "Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data," Mathematics, MDPI, vol. 10(5), pages 1-18, February.
    4. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2018. "Semiparametric regression analysis of interval‐censored data with informative dropout," Biometrics, The International Biometric Society, vol. 74(4), pages 1213-1222, December.
    5. Shuying Wang & Chunjie Wang & Jianguo Sun, 2021. "An additive hazards cure model with informative interval censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 244-268, April.
    6. John D. Rice & Robert L. Strawderman & Brent A. Johnson, 2018. "Regularity of a renewal process estimated from binary data," Biometrics, The International Biometric Society, vol. 74(2), pages 566-574, June.
    7. Douglas E. Schaubel & Bin Nan, 2018. "Special issue dedicated to Jack Kalbfleisch," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 1-2, January.
    8. Mengzhu Yu & Mingyue Du, 2022. "Regression Analysis of Multivariate Interval-Censored Failure Time Data under Transformation Model with Informative Censoring," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    9. Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2018. "Semiparametric analysis of the additive hazards model with informatively interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 1-9.
    10. Zhao, Hui & Sun, Dayu & Li, Gang & Sun, Jianguo, 2019. "Simultaneous estimation and variable selection for incomplete event history studies," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 350-361.
    11. C. Dean & E. Juarez Colunga, 2011. "Comments on: Nonparametric inference based on panel count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 43-45, May.
    12. Chi‐Chung Wen & Yi‐Hau Chen, 2018. "Pseudo and conditional score approach to joint analysis of current count and current status data," Biometrics, The International Biometric Society, vol. 74(4), pages 1223-1231, December.
    13. Du, Mingyue & Zhao, Xingqiu & Sun, Jianguo, 2022. "Variable selection for case-cohort studies with informatively interval-censored outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    14. Wang, Shuying & Wang, Chunjie & Wang, Peijie & Sun, Jianguo, 2020. "Estimation of the additive hazards model with case K interval-censored failure time data in the presence of informative censoring," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

    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:gam:jmathe:v:12:y:2024:i:23:p:3667-:d:1527392. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.