IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v56y2000i3p940-943.html
   My bibliography  Save this article

A Proportional Hazards Model for Multivariate Interval-Censored Failure Time Data

Author

Listed:
  • William B. Goggins
  • Dianne M. Finkelstein

Abstract

No abstract is available for this item.

Suggested Citation

  • William B. Goggins & Dianne M. Finkelstein, 2000. "A Proportional Hazards Model for Multivariate Interval-Censored Failure Time Data," Biometrics, The International Biometric Society, vol. 56(3), pages 940-943, September.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:3:p:940-943
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.00940.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. William B. Goggins & Dianne M. Finkelstein & Alan M. Zaslavsky, 1999. "Applying the Cox Proportional Hazards Model When the Change Time of a Binary Time-Varying Covariate Is Interval Censored," Biometrics, The International Biometric Society, vol. 55(2), pages 445-451, June.
    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. Tianyi Lu & Shuwei Li & Liuquan Sun, 2023. "Combined estimating equation approaches for the additive hazards model with left-truncated and interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 672-697, July.
    2. Chen, Ling & Sun, Jianguo, 2010. "A multiple imputation approach to the analysis of interval-censored failure time data with the additive hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1109-1116, April.
    3. Richard J. Cook & Leilei Zeng & Ker-Ai Lee, 2008. "A Multistate Model for Bivariate Interval-Censored Failure Time Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1100-1109, December.
    4. Ying Zhang & Lei Hua & Jian Huang, 2010. "A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 338-354, June.
    5. Zhao, Xingqiu & Duan, Ran & Zhao, Qiang & Sun, Jianguo, 2013. "A new class of generalized log rank tests for interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 123-131.
    6. Baihua He & Yanyan Liu & Yuanshan Wu & Xingqiu Zhao, 2020. "Semiparametric efficient estimation for additive hazards regression with case II interval-censored survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 708-730, October.
    7. Yichen Lou & Peijie Wang & Jianguo Sun, 2023. "A semi-parametric weighted likelihood approach for regression analysis of bivariate interval-censored outcomes from case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 628-653, July.
    8. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2017. "Semiparametric estimation of the accelerated failure time model with partly interval‐censored data," Biometrics, The International Biometric Society, vol. 73(4), pages 1161-1168, December.
    9. Donglin Zeng & Fei Gao & D. Y. Lin, 2017. "Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data," Biometrika, Biometrika Trust, vol. 104(3), pages 505-525.
    10. Gamage, Prabhashi W. Withana & McMahan, Christopher S. & Wang, Lianming & Tu, Wanzhu, 2018. "A Gamma-frailty proportional hazards model for bivariate interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 354-366.
    11. Deng, Dianliang & Fang, Hong-Bin, 2009. "Asymptotics for non-parametric likelihood estimation with doubly censored multivariate failure times," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1802-1815, September.
    12. Lambert, Philippe, 2011. "Smooth semiparametric and nonparametric Bayesian estimation of bivariate densities from bivariate histogram data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 429-445, January.
    13. Qingning Zhou & Tao Hu & Jianguo Sun, 2017. "A Sieve Semiparametric Maximum Likelihood Approach for Regression Analysis of Bivariate Interval-Censored Failure Time Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 664-672, April.
    14. 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.
    15. David B. Dunson & Gregg E. Dinse, 2002. "Bayesian Models for Multivariate Current Status Data with Informative Censoring," Biometrics, The International Biometric Society, vol. 58(1), pages 79-88, March.
    16. Yan Chen & Yulu Zhao, 2021. "Efficient sparse estimation on interval-censored data with approximated L0 norm: Application to child mortality," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    17. Kaitlyn Cook & Wenbin Lu & Rui Wang, 2023. "Marginal proportional hazards models for clustered interval‐censored data with time‐dependent covariates," Biometrics, The International Biometric Society, vol. 79(3), pages 1670-1685, September.
    18. Yang-Jin Kim, 2014. "Regression analysis of recurrent events data with incomplete observation gaps," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1619-1626, July.
    19. Wang, Naichen & Wang, Lianming & McMahan, Christopher S., 2015. "Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 140-150.
    20. Vu, Hien T. V., 2004. "Estimation in semiparametric conditional shared frailty models with events before study entry," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 621-637, April.

    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. repec:jss:jstsof:36:i02 is not listed on IDEAS
    2. Rebecca A. Betensky & Dianne M. Finkelstein, 2002. "Testing for Dependence Between Failure Time and Visit Compliance with Interval-Censored Data," Biometrics, The International Biometric Society, vol. 58(1), pages 58-63, March.
    3. Anne Eaton & Yifei Sun & James Neaton & Xianghua Luo, 2022. "Nonparametric estimation in an illness‐death model with component‐wise censoring," Biometrics, The International Biometric Society, vol. 78(3), pages 1168-1180, September.
    4. Dianne M. Finkelstein & Rui Wang & Linda H. Ficociello & David A. Schoenfeld, 2010. "A Score Test for Association of a Longitudinal Marker and an Event with Missing Data," Biometrics, The International Biometric Society, vol. 66(3), pages 726-732, September.
    5. Richard J. Cook & Leilei Zeng & Ker-Ai Lee, 2008. "A Multistate Model for Bivariate Interval-Censored Failure Time Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1100-1109, December.
    6. Chen, Baojiang & Zhou, Xiao-Hua, 2013. "A correlated random effects model for non-homogeneous Markov processes with nonignorable missingness," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 1-13.
    7. Doug Morrison & Oliver Laeyendecker & Ron Brookmeyer, 2022. "Regression with interval‐censored covariates: Application to cross‐sectional incidence estimation," Biometrics, The International Biometric Society, vol. 78(3), pages 908-921, September.
    8. Dianne M. Finkelstein & William B. Goggins & David A. Schoenfeld, 2002. "Analysis of Failure Time Data with Dependent Interval Censoring," Biometrics, The International Biometric Society, vol. 58(2), pages 298-304, June.
    9. Lu Tian & Stephen Lagakos, 2006. "Analysis of a Partially Observed Binary Covariate Process and a Censored Failure Time in the Presence of Truncation and Competing Risks," Biometrics, The International Biometric Society, vol. 62(3), pages 821-828, September.

    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:56:y:2000:i:3:p:940-943. 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.