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Semiparametric regression of multivariate panel count data with informative observation times

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  • 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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    Cited by:

    1. Chunling Wang & Xiaoyan Lin, 2022. "Bayesian Semiparametric Regression Analysis of Multivariate Panel Count Data," Stats, MDPI, vol. 5(2), pages 1-17, May.
    2. 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.
    3. Weiwei Wang & Yijun Wang & Xiaobing Zhao, 2022. "Semiparametric analysis of multivariate panel count data with nonlinear interactions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 89-115, January.
    4. Weiwei Wang & Zhiyang Cui & Ruijie Chen & Yijun Wang & Xiaobing Zhao, 2024. "Regression analysis of clustered panel count data with additive mean models," Statistical Papers, Springer, vol. 65(5), pages 2915-2936, July.
    5. 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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