Full Text Available

Access Repository
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Cover Image

Longitudinal latent class and joint modelling of antiretroviral adherence

This dissertation investigates the relationship between antiretroviral therapy (ART) adherence and viral outcomes in people living with HIV in South Africa using advanced statistical modeling techniques. Utilizing data from the ADD-ART study, a prospective cohort of 238 adults on ART in Cape Town, t...

Full description

Saved in:
Main Author: Mcduling, Campbell
Other Authors: Little, Francesca
Format: Thesis
Language:English
Eng
Published: Department of Statistical Sciences 2025
Subjects:
statistical science
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1867613314942500864
access_status_str Open Access
author Mcduling, Campbell
author2 Little, Francesca
author_browse Little, Francesca
Mcduling, Campbell
author_facet Little, Francesca
Mcduling, Campbell
author_sort Mcduling, Campbell
collection Thesis
description This dissertation investigates the relationship between antiretroviral therapy (ART) adherence and viral outcomes in people living with HIV in South Africa using advanced statistical modeling techniques. Utilizing data from the ADD-ART study, a prospective cohort of 238 adults on ART in Cape Town, the research employs survival analysis, joint modeling, and longitudinal latent class analysis to compare di↵erent adherence monitoring tools and examine heterogeneity in adherence behaviors. Key findings include: Electronic Adherence Monitoring (EAM) and tenofovir diphosphate levels in dried blood spots were more strongly associated with viral non-suppression than self-reported adherence; joint modeling revealed a stronger association between EAM adherence and viral outcomes compared to traditional survival models, with each additional missed dose in the preceding 30 days associated with an 81% increase in the hazard of viral non-suppression; longitudinal latent class analysis identified five distinct adherence trajectory groups, with poorer or declining adherence groups experiencing significantly higher rates of viral non-suppression; baseline viral load and prior tuberculosis exposure were significant predictors of subsequent viral non-suppression, even after accounting for adherence. The results highlight the importance of using objective adherence measures, the value of advanced statistical techniques in HIV research, and the need for personalized adherence support strategies. Limitations include potential violations of model assumptions and generalizability constraints.
format Thesis
id oai:open.uct.ac.za:11427/42472
institution University of Cape Town (South Africa)
language English
Eng
last_indexed 2026-06-10T12:34:10.861Z
license_str Not specified — see source repository
provenance_str_mv Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository
publishDate 2025
publishDateRange 2025
publishDateSort 2025
publisher Department of Statistical Sciences
publisherStr Department of Statistical Sciences
record_format dspace
source_str UCTD — University of Cape Town Open Access Repository
spelling oai:open.uct.ac.za:11427/42472 Longitudinal latent class and joint modelling of antiretroviral adherence Mcduling, Campbell Little, Francesca Orrell, Catherine statistical science This dissertation investigates the relationship between antiretroviral therapy (ART) adherence and viral outcomes in people living with HIV in South Africa using advanced statistical modeling techniques. Utilizing data from the ADD-ART study, a prospective cohort of 238 adults on ART in Cape Town, the research employs survival analysis, joint modeling, and longitudinal latent class analysis to compare di↵erent adherence monitoring tools and examine heterogeneity in adherence behaviors. Key findings include: Electronic Adherence Monitoring (EAM) and tenofovir diphosphate levels in dried blood spots were more strongly associated with viral non-suppression than self-reported adherence; joint modeling revealed a stronger association between EAM adherence and viral outcomes compared to traditional survival models, with each additional missed dose in the preceding 30 days associated with an 81% increase in the hazard of viral non-suppression; longitudinal latent class analysis identified five distinct adherence trajectory groups, with poorer or declining adherence groups experiencing significantly higher rates of viral non-suppression; baseline viral load and prior tuberculosis exposure were significant predictors of subsequent viral non-suppression, even after accounting for adherence. The results highlight the importance of using objective adherence measures, the value of advanced statistical techniques in HIV research, and the need for personalized adherence support strategies. Limitations include potential violations of model assumptions and generalizability constraints. 2025-12-22T08:19:32Z 2025-12-22T08:19:32Z 2025 2025-12-22T08:15:02Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/42472 en Eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town
spellingShingle statistical science
Mcduling, Campbell
Longitudinal latent class and joint modelling of antiretroviral adherence
thesis_degree_str Master's
title Longitudinal latent class and joint modelling of antiretroviral adherence
title_full Longitudinal latent class and joint modelling of antiretroviral adherence
title_fullStr Longitudinal latent class and joint modelling of antiretroviral adherence
title_full_unstemmed Longitudinal latent class and joint modelling of antiretroviral adherence
title_short Longitudinal latent class and joint modelling of antiretroviral adherence
title_sort longitudinal latent class and joint modelling of antiretroviral adherence
topic statistical science
url http://hdl.handle.net/11427/42472
work_keys_str_mv AT mcdulingcampbell longitudinallatentclassandjointmodellingofantiretroviraladherence

Similar Items