ArticleBMC infectious diseases2020
Joint modelling of longitudinal and time-to-event data: an illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy.
Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Regularized regression models for accurate prediction of HIV progression in ART patients: a comparative study.Scientific reports · 2026Article
- Determinants of CD4 count recovery among severely immunosuppressed HIV patients initiated on antiretroviral therapy: a prospective cohort study in KwaZulu-Natal, South Africa.Frontiers in public health · 2026Article
- Investigating the Determinants of Mortality before CD4 Count Recovery in a Cohort of Patients Initiated on Antiretroviral Therapy in South Africa Using a Fine and Gray Competing Risks Model.Tropical medicine and infectious disease · 2024Article
- Joint modeling of longitudinal CD4 count data and time to first occurrence of composite outcome.Journal of clinical tuberculosis and other mycobacterial diseases · 2024Article
- Dynamic predictions from longitudinal CD4 count measures and time to death of HIV/AIDS patients using a Bayesian joint model.Scientific African · 2023Article
- Using joint models to study the association between CD4 count and the risk of death in TB/HIV data.BMC medical research methodology · 2022Article
- A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics.CPT: pharmacometrics & systems pharmacology · 2022Article
- Serum Albumin Trends in Relation With Prognosis of Individuals Receiving Hemodialysis Therapy.Cureus · 2021Article
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5 authors.
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Abstract
backgroundModelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality.
methodsWe studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model.
resultsUsing joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, (HR: 1.34, 95% CI: 1.27-1.42). Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, (HR: 1.17, 95% CI: 1.12-1.23).
conclusionsJoint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality.
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