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Analysis of the HIV/AIDS Data Using Joint Modeling of Longitudinal (k,l)-Inflated Count and Time to Event Data in Clinical Trials

Author

Listed:
  • Mojtaba Zeinali Najafabadi

    (Shahid Beheshti University)

  • Ehsan Bahrami Samani

    (Shahid Beheshti University)

Abstract

Generalized linear mixed effect models (GLMEMs) are widely applied for the analysis of correlated non-Gaussian data such as those found in longitudinal studies. On the other hand, the Cox (proportional hazards, PHs) and the accelerated failure time (AFT) regression models are two well-known approaches in survival analysis to modeling time to event (TTE) data. In this article, we develop joint modeling of longitudinal count (LC) and TTE data and consider extensions with fixed effects and parametric random effects in our proposed joint models. The LC response is inflated in two points k and l (k

Suggested Citation

  • Mojtaba Zeinali Najafabadi & Ehsan Bahrami Samani, 2025. "Analysis of the HIV/AIDS Data Using Joint Modeling of Longitudinal (k,l)-Inflated Count and Time to Event Data in Clinical Trials," Annals of Data Science, Springer, vol. 12(2), pages 695-719, April.
    • Handle: RePEc:spr:aodasc:v:12:y:2025:i:2:d:10.1007_s40745-024-00532-5
    DOI: 10.1007/s40745-024-00532-5
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