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Joint generalized estimating equations for longitudinal binary data

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  • Huang, Youjun
  • Pan, Jianxin

Abstract

Modeling longitudinal binary data is challenging but common in practice. Existing methods on modeling of binary responses take no account of the fact that the correlation coefficient of binary responses must have an upper bound which is smaller than one. Ignoring this fact can lead to incorrect statistical inferences for longitudinal binary data. A novel method is proposed to model the mean and within-subject correlation coefficients for longitudinal binary data, simultaneously, by taking into account the constraints of the upper bounds. By introducing latent normally distributed random variables, the correlation coefficients of binary responses are connected to those for the latent variables, of which the correlation coefficients are modeled accordingly. A joint generalized estimating equation (GEE) method is developed for this purpose and the resulting correlation coefficients are shown to satisfy the constraints. Asymptotic normality of the parameter estimators is derived and simulation studies are made under various scenarios, showing that the proposed joint GEE method works very well even if the working covariance structures are misspecified. For illustration, the proposed method is applied to two real data practices to assess the effects of covariates on the mean and within-subject correlation coefficients.

Suggested Citation

  • Huang, Youjun & Pan, Jianxin, 2021. "Joint generalized estimating equations for longitudinal binary data," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    • Handle: RePEc:eee:csdana:v:155:y:2021:i:c:s0167947320302012
    DOI: 10.1016/j.csda.2020.107110
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    References listed on IDEAS

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    1. Brajendra C. Sutradhar & Kalyan Das, 2000. "On the Accuracy of Efficiency of Estimating Equation Approach," Biometrics, The International Biometric Society, vol. 56(2), pages 622-625, June.
    2. Zengri Wang, 2003. "Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function," Biometrika, Biometrika Trust, vol. 90(4), pages 765-775, December.
    3. Huajun Ye & Jianxin Pan, 2006. "Modelling of covariance structures in generalised estimating equations for longitudinal data," Biometrika, Biometrika Trust, vol. 93(4), pages 927-941, December.
    4. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    5. Lan Wang & Jianhui Zhou & Annie Qu, 2012. "Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis," Biometrics, The International Biometric Society, vol. 68(2), pages 353-360, June.
    6. N. Rao Chaganty & Harry Joe, 2004. "Efficiency of generalized estimating equations for binary responses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 851-860, November.
    7. Patrick J. Heagerty, 1999. "Marginally Specified Logistic-Normal Models for Longitudinal Binary Data," Biometrics, The International Biometric Society, vol. 55(3), pages 688-698, September.
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    Cited by:

    1. Peng, Cheng & Yang, Yihe & Zhou, Jie & Pan, Jianxin, 2022. "Latent Gaussian copula models for longitudinal binary data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

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