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Regression analysis of multivariate recurrent event data with time-varying covariate effects

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  • Sun, Liuquan
  • Zhu, Liang
  • Sun, Jianguo

Abstract

Recurrent event data occur in many fields and many approaches have been proposed for their analyses (Andersen et al. (1993)Â [1]; Cook and Lawless (2007)Â [3]). However, most of the available methods allow only time-independent covariate effects, and sometimes this may not be true. In this paper, we consider regression analysis of multivariate recurrent event data in which some covariate effects may be time-dependent. For the problem, we employ the marginal modeling approach and, especially, estimating equation-based inference procedures are developed. Both asymptotic and finite-sample properties of the proposed estimates are established and an illustrative example is provided.

Suggested Citation

  • Sun, Liuquan & Zhu, Liang & Sun, Jianguo, 2009. "Regression analysis of multivariate recurrent event data with time-varying covariate effects," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2214-2223, November.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:10:p:2214-2223
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    References listed on IDEAS

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    1. Thomas H. Scheike & Torben Martinussen, 2004. "On Estimation and Tests of Time‐Varying Effects in the Proportional Hazards Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 51-62, March.
    2. Limin X. Clegg & Jianwen Cai & Pranab K. Sen, 1999. "A Marginal Mixed Baseline Hazards Model for Multivariate Failure Time Data," Biometrics, The International Biometric Society, vol. 55(3), pages 805-812, September.
    3. Lin D Y & Ying Z, 2001. "Semiparametric and Nonparametric Regression Analysis of Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 103-126, March.
    4. Lin D Y & Wei L J & Ying Z, 2001. "Semiparametric Transformation Models for Point Processes," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 620-628, June.
    5. 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.
    6. Douglas Schaubel & Jianwen Cai, 2006. "Rate/Mean Regression for Multiple‐Sequence Recurrent Event Data with Missing Event Category," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 191-207, June.
    7. Torben Martinussen & Thomas H. Scheike & Ib M. Skovgaard, 2002. "Efficient Estimation of Fixed and Time‐varying Covariate Effects in Multiplicative Intensity Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 57-74, March.
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    Cited by:

    1. Xiaomeng Qi & Zhangsheng Yu, 2023. "Kernel regression for cause-specific hazard models with time-dependent coefficients," Computational Statistics, Springer, vol. 38(1), pages 263-283, March.
    2. Ye, Peng & Zhao, Xingqiu & Sun, Liuquan & Xu, Wei, 2015. "A semiparametric additive rates model for multivariate recurrent events with missing event categories," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 39-50.
    3. Liang Zhu & Sangbum Choi & Yimei Li & Xuelin Huang & Jianguo Sun & Leslie L. Robison, 2020. "Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 820-832, October.
    4. Jing Ning & Chunyan Cai & Yong Chen & Xuelin Huang & Mei‐Cheng Wang, 2020. "Semiparametric modelling and estimation of covariate‐adjusted dependence between bivariate recurrent events," Biometrics, The International Biometric Society, vol. 76(4), pages 1229-1239, December.
    5. Xingqiu Zhao & N. Balakrishnan & Jianguo Sun, 2011. "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 1-42, May.

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