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Generalized Accelerated Failure Time Models for Recurrent Events

Author

Listed:
  • Xiaoyi Wen

    (The Institute of Statistics and Big Data, Renmin University of China, Beijing 100872, China)

  • Jinfeng Xu

    (School of Mathematics, Yunnan Normal University, Kunming 650092, China)

Abstract

For analyzing recurrent event data, we consider a generalization of the classical accelerated failure time model. In the proposed approach, the general function is no longer assumed to be a singleton but allowed to be time-varying. This is in the same spirit as in quantile regression and the counting process techniques can be utilized. Theoretical properties such as consistency and asymptotic normality are obtained. The illustration of the methodology using simulation studies and then the application to the bladder cancer data is also given.

Suggested Citation

  • Xiaoyi Wen & Jinfeng Xu, 2022. "Generalized Accelerated Failure Time Models for Recurrent Events," Mathematics, MDPI, vol. 10(15), pages 1-14, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2662-:d:874560
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    References listed on IDEAS

    as
    1. Wang, Huixia Judy & Wang, Lan, 2009. "Locally Weighted Censored Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1117-1128.
    2. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    3. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Yijian Huang & Limin Peng, 2009. "Accelerated Recurrence Time Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 636-648, December.
    6. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    7. Xiaoyan Sun & Limin Peng & Yijian Huang & HuiChuan J. Lai, 2016. "Generalizing Quantile Regression for Counting Processes With Applications to Recurrent Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 145-156, March.
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