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

Mojtaba Zeinali Najafabadi and Ehsan Bahrami Samani ()
Additional contact information
Mojtaba Zeinali Najafabadi: Shahid Beheshti University
Ehsan Bahrami Samani: Shahid Beheshti University

Annals of Data Science, 2025, vol. 12, issue 2, No 12, 695-719

Abstract: 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

Keywords: Generalized linear mixed effect model (GLMEM); Longitudinal count (LC) data; Time to event (TTE) data; Joint model; Cox (proportional hazards or PHs) model; Accelerated failure time (AFT) model; Power series distribution (PSD) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-024-00532-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:12:y:2025:i:2:d:10.1007_s40745-024-00532-5

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-024-00532-5

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().