Skip to main content

Advertisement

Log in

A Semiparametric Additive-multiplicative Rates Model for the Weighted Composite Endpoint of Recurrent and Terminal Events

  • Published:
Acta Mathematica Sinica, English Series Aims and scope Submit manuscript

Abstract

Recurrent event data are commonly encountered in many scientific fields, including biomedical studies, clinical trials and epidemiological surveys, and many statistical methods have been proposed for their analysis. In this paper, we consider to use a weighted composite endpoint of recurrent and terminal events, which is weighted by the severity of each event, to assess the overall effects of covariates on the two types of events. A flexible additive-multiplicative model incorporating both multiplicative and additive effects on the rate function is proposed to analyze such weighted composite event process, and more importantly, the dependence structure among the recurrent and terminal events is left unspecified. For the estimation, we construct the unbiased estimating equations by virtue of the inverse probability weighting technique, and the resulting estimators are shown to be consistent and asymptotically normal under some mild regularity conditions. We evaluate the finite-sample performance of the proposed method via simulation studies and apply the proposed method to a set of real data arising from a bladder cancer study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Andersen, P. K., Gill, R. D.: Cox’s regression model for counting processes: a large sample study. Ann. Stat., 10, 1100–1120 (1982)

    Article  MathSciNet  Google Scholar 

  2. Byar, D. P.: The Veterans Administration Study of Chemoprophylaxis for Recurrent Stage I Bladder Tumours: Comparisons of Placebo, Pyridoxine and Topical Thiotepa, Springer, New York, 1980

    Google Scholar 

  3. Cai, Z. J.: A semiparametric additive rate model for recurrent events with an informative terminal event. Biometrika, 97, 699–712 (2010)

    Article  MathSciNet  Google Scholar 

  4. Cook, J. R., Lawless, J. F.: Marginal analysis of recurrent events and a terminating event. Stat. Med., 16, 911–924 (1997)

    Article  Google Scholar 

  5. Cook, R. J., Lawless, J. F., Lakhal–Chaieb, L., et al.: Robust estimation of mean functions and treatment effects for recurrent events under event-dependent censoring and termination: application to skeletal complications in cancer metastatic to bone. J. Amer. Statist. Assoc., 104, 60–75 (2009)

    Article  MathSciNet  Google Scholar 

  6. Cox, D. R.: Regression models and life tables. J. R. Stat. Soc. Ser. B., 34, 187–200 (1972)

    MathSciNet  Google Scholar 

  7. Ghosh, D., Lin, D. Y.: Nonparametric analysis of recurrent events and death. Biometrics, 56, 554–562 (2000)

    Article  MathSciNet  Google Scholar 

  8. Ghosh, D., Lin, D. Y.: Marginal regression models for recurrent and terminal events. Stat. Sin., 12, 663–688 (2002)

    MathSciNet  Google Scholar 

  9. Han, M., Sun, L. Q., Liu, Y. T., et al.: Joint analysis of recurrent event data with additive-multiplicative hazards model for the terminal event time. Metrika, 81, 523–547 (2018)

    Article  MathSciNet  Google Scholar 

  10. Harrington, D. P., Fleming, T. R.: Counting Processes and Survival Analysis, Wiley, New York, 1991

    Google Scholar 

  11. Kalbfleisch, J. D., Schaubel, D. E., Ye, Y., et al.: An estimating function approach to the analysis of recurrent and terminal events. Biometrics, 69, 366–374 (2013)

    Article  MathSciNet  Google Scholar 

  12. Kalbfleisch, J. D., Prentice, R. L.: The Statistical Analysis of Failure Time Data, 2nd Edn, Wiley, New York, 2002

    Book  Google Scholar 

  13. Lawless, J. F., Nadeau, C.: Some simple robust methods for the analysis of recurrent events. J. Amer. Statist. Assoc., 37, 158–168 (1995)

    MathSciNet  Google Scholar 

  14. Li, Q. H., Lagakos, S. W.: Use of the Wei-Lin-Weissfeld method for the analysis of a recurring and a terminating event. Stat. Med., 16, 925–940 (1997)

    Article  Google Scholar 

  15. Lin, D. Y., Wei, J. L., Yang, I., et al.: Semiparametric regression for the mean and rate functions of recurrent events. J. Amer. Statist. Assoc., 62, 711–730 (2000)

    MathSciNet  Google Scholar 

  16. Lin, D. Y., Ying, Z. L.: Semiparametric analysis of general additive-multiplicative hazard models for counting processes. Ann. Stat., 23, 1712–1734 (1995)

    Article  MathSciNet  Google Scholar 

  17. Liu, L., Wolfe, R. A., Huang, X. L.: Shared frailty models for recurrent events and a terminal event. Biometrics, 60, 747–756 (2004)

    Article  MathSciNet  Google Scholar 

  18. Liu, Y., Wu, Y., Cai, J., et al.: Additive-multiplicative rates model for recurrent events. Lifetime Data. Anal., 16, 353 (2010)

    Article  MathSciNet  Google Scholar 

  19. Mao, L., Lin, D. Y.: Semiparametric regression for the weighted composite endpoint of recurrent and terminal events. Biostatistics, 17, 390–403 (2016)

    Article  MathSciNet  Google Scholar 

  20. Neaton, J. D., Gray, G., Zuckerman, B. D., et al.: Key issues in end point selection for heart failure trials: Composite end points. J. Card. Failure., 11, 567–575 (2005)

    Article  Google Scholar 

  21. Pepe, M. S., Cai, J. W.: Some graphical displays and marginal regression analyses for recurrent failure times and time dependent covariates. J. Amer. Statist. Assoc., 88, 811–820 (1993)

    Article  Google Scholar 

  22. Prentice, R. L., Williams, B. J., Peterson, A. V.: On the regression analysis of multivariate failure time data. Biometrika, 68, 373–379 (1981)

    Article  MathSciNet  Google Scholar 

  23. Qu, L., Sun, L., Liu, L.: Joint modeling of recurrent and terminal events using additive models. Stat. Interface., 10, 699–710 (2017)

    Article  MathSciNet  Google Scholar 

  24. Robins, J., Rotnitzky, A.: Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers, Birkhäuser, Boston, 1992

    Book  Google Scholar 

  25. Schaubel, D. E., Zeng, D. L., Cai, J. W.: A semiparametric additive rates model for recurrent event data. Lifetime Data. Anal., 12, 389–406 (2006)

    Article  MathSciNet  Google Scholar 

  26. Sun, L. Q., Kang, F.: An additive-multiplicative rates model for recurrent event data with informative terminal event. Lifetime Data. Anal., 19, 117–137 (2013)

    Article  MathSciNet  Google Scholar 

  27. Sun, X. W., Ding, J. L., Sun, L. Q.: A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events. Lifetime Data. Anal., 26, 471–492 (2019)

    Article  MathSciNet  Google Scholar 

  28. van der Vaart, A. W., Wellner, J. A.: Weak Convergence and Empirical Processes, Springer, New York, 1996

    Book  Google Scholar 

  29. Wang, M.-C., Qin, J., Chiang, C.-T.: Analyzing recurrent event data with informative censoring. J. Amer. Statist. Assoc., 96, 1057–1065 (2001)

    Article  MathSciNet  Google Scholar 

  30. Xu, G. J., Chiou, S.-H., Huang, C.-Y., et al.: Joint scale-change models for recurrent events and failure time. J. Amer. Statist. Assoc., 112, 794–805 (2017)

    Article  MathSciNet  Google Scholar 

  31. Ye, P., Sun, L. Q., Zhao, X. Q., et al.: An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random. Sci. China Math., 58, 1163–1178 (2015)

    Article  MathSciNet  Google Scholar 

  32. Yu, G. L., Li, Y., Zhu, L., et al., Robison, L. L.: An additive-multiplicative mean model for panel count data with dependent observation and dropout processes. Scand. J. Stat., 46, 414–431 (2018)

    Article  Google Scholar 

  33. Zeng, D. L., Lin, D. Y.: Semiparametric transformation models with random effects for recurrent events. J. Amer. Statist. Assoc., 102, 167–180 (2007)

    Article  MathSciNet  Google Scholar 

  34. Zeng, D. L., Lin, D. Y.: Semiparametric transformation models with random effects for joint analysis of recurrent and terminal events. Biometrics, 65, 746–752 (2009)

    Article  MathSciNet  Google Scholar 

  35. Zhao, X. Q., Zhou, J., Sun, L. Q.: Semiparametric transformation models with time-varying coefficients for recurrent and terminal events. Biometrics, 67, 404–414 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiang Xiong.

Ethics declarations

Conflict of Interest The authors declare no conflict of interest.

Additional information

Supported by the National Natural Science Foundation of China (Grant Nos. 11771431, 11690015, 11926341, 11731015, 11901128 and 11601097), Key Laboratory of RCSDS, CAS (Grant No. 2008DP173182), Natural Science Foundation of Guangdong Province of China (Grant Nos. 2018A030310068, 2021A1515010044), University Innovation Team Project of Guangdong Province (Grant No. 2020WCXTD018), Science and Technology Program of Guangzhou, China (Grant Nos. 202102020368, 202102010512)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, Y., Xiong, Q. & Li, S.W. A Semiparametric Additive-multiplicative Rates Model for the Weighted Composite Endpoint of Recurrent and Terminal Events. Acta. Math. Sin.-English Ser. 40, 985–999 (2024). https://doi.org/10.1007/s10114-023-1170-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10114-023-1170-6

Keywords

MR(2010) Subject Classification

Navigation