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Semiparametric regression of multivariate panel count data with informative observation times

Author

Listed:
  • Li, Yang
  • He, Xin
  • Wang, Haiying
  • Zhang, Bin
  • Sun, Jianguo

Abstract

Multivariate panel count data occur in many fields such as medical and social science studies in which several outcomes of interest are measured simultaneously and repeatedly over time. When the observation times are not pre-specified, it is very likely that either the observation or follow-up times are informative about the response process. In such situations, most existing approaches either specify a dependence structure with some fixed distributions or assume independence given some covariates, which may not be true and result in misleading conclusions. In this paper, we present a joint modeling approach that allows the possible mutual correlations to be characterized by time-dependent random effects. Estimating equations are developed for the parameter estimation and the resulted estimators are shown to be consistent and asymptotically normal. The finite sample performance of the proposed estimators is assessed through a simulation study and an illustrative example from a maternal influenza immunization study on infant growth is provided.

Suggested Citation

  • Li, Yang & He, Xin & Wang, Haiying & Zhang, Bin & Sun, Jianguo, 2015. "Semiparametric regression of multivariate panel count data with informative observation times," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 209-219.
  • Handle: RePEc:eee:jmvana:v:140:y:2015:i:c:p:209-219
    DOI: 10.1016/j.jmva.2015.05.014
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    References listed on IDEAS

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    1. Jianguo Sun & Xingwei Tong & Xin He, 2007. "Regression Analysis of Panel Count Data with Dependent Observation Times," Biometrics, The International Biometric Society, vol. 63(4), pages 1053-1059, December.
    2. Xingqiu Zhao & Jie Zhou & Liuquan Sun, 2011. "Semiparametric Transformation Models with Time-Varying Coefficients for Recurrent and Terminal Events," Biometrics, The International Biometric Society, vol. 67(2), pages 404-414, June.
    3. 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.
    4. J. Sun & L. J. Wei, 2000. "Regression analysis of panel count data with covariate‐dependent observation and censoring times," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 293-302.
    5. Liuquan Sun & Xinyuan Song & Jie Zhou & Lei Liu, 2012. "Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 688-700, June.
    6. Ying Zhang, 2002. "A semiparametric pseudolikelihood estimation method for panel count data," Biometrika, Biometrika Trust, vol. 89(1), pages 39-48, March.
    7. Zhang, Haixiang & Zhao, Hui & Sun, Jianguo & Wang, Dehui & Kim, KyungMann, 2013. "Regression analysis of multivariate panel count data with an informative observation process," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 71-80.
    8. Zhao, Xingqiu & Tong, Xingwei, 2011. "Semiparametric regression analysis of panel count data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 291-300, January.
    9. Hui Zhao & Yang Li & Jianguo Sun, 2013. "Semiparametric analysis of multivariate panel count data with dependent observation processes and a terminal event," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 379-394, June.
    10. Zhao, Xingqiu & Tong, Xingwei & Sun, Jianguo, 2013. "Robust estimation for panel count data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 33-40.
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    Cited by:

    1. Hangjin Jiang & Wen Su & Xingqiu Zhao, 2020. "Robust estimation for panel count data with informative observation times and censoring times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 65-84, January.
    2. Deng, Shirong & Liu, Kin-yat & Zhao, Xingqiu, 2017. "Semiparametric regression analysis of multivariate longitudinal data with informative observation times," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 120-130.

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