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Multiple-response logistic regression modeling with application to an analysis of cirrhosis liver disease data

Author

Listed:
  • Yang Jing-Nan

    (Northwest Normal University
    Gansu Provincial Research Center for Basic Disciplines of Mathematics and Statistics)

  • Tian Yu-Zhu

    (Northwest Normal University
    Gansu Provincial Research Center for Basic Disciplines of Mathematics and Statistics)

  • Wang Yue

    (The Education University of Hong Kong)

  • Wu Chun-Ho

    (The Hang Seng University of Hong Kong)

Abstract

In practical data analysis, individual measurements usually include two or more responses, and some statistical correlations often exist between the responses. Especially in medical data analysis, observations are often binary responses. A class of multi-response logistic regression model based on a joint modeling approach is investigated in this paper, and an application to a group data of primary biliary cirrhosis diseases is considered. Firstly, we propose a new class of multi-response logistic distribution and investigate its statistical properties. Secondly, a multi-response logistic regression model is constructed using a latent variable model and multi-variate logistic error distribution. Furthermore, the parameter estimation method of the model is provided by applying the monte carlo expectation maximization (MCEM) algorithm and the multiple imputation method. Finally, numerical simulations and comparative predictions on a test set are performed to validate the finite sample performance of the proposed model, and the model is applied to a cirrhosis disease dataset for analysis.

Suggested Citation

  • Yang Jing-Nan & Tian Yu-Zhu & Wang Yue & Wu Chun-Ho, 2025. "Multiple-response logistic regression modeling with application to an analysis of cirrhosis liver disease data," Computational Statistics, Springer, vol. 40(5), pages 2611-2634, June.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:5:d:10.1007_s00180-024-01575-1
    DOI: 10.1007/s00180-024-01575-1
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    References listed on IDEAS

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    1. Le Wang & Matthew L. Williams & Yong Chen & Jinbo Chen, 2020. "Novel two‐phase sampling designs for studying binary outcomes," Biometrics, The International Biometric Society, vol. 76(1), pages 210-223, March.
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    3. S. Satterthwaite & T. Hutchinson, 1978. "A generalisation of Gumbel's bivariate logistic distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 25(1), pages 163-170, December.
    4. Gul Inan & Ozlem Ilk, 2019. "A marginalized multilevel model for bivariate longitudinal binary data," Statistical Papers, Springer, vol. 60(3), pages 601-628, June.
    5. Ali, Mir M. & Mikhail, N. N. & Haq, M. Safiul, 1978. "A class of bivariate distributions including the bivariate logistic," Journal of Multivariate Analysis, Elsevier, vol. 8(3), pages 405-412, September.
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