ArticleScientific reports2020
Negative binomial mixed models for analyzing longitudinal CD4 count data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
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- Correlates of antenatal care visits among expectant mothers in Malawi: application of generalised estimating equations and likelihood-based poisson and negative binomial mixed models.Reproductive health · 2026Article
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- Temporal trends and determinants of ED visits in sickle cell disease: a multicenter EHR study from Saudi Arabia (2016-2021).Frontiers in medicine · 2026Article
- Negative binomial mixed effects location-scale models for intensive longitudinal count-type physical activity data provided by wearable devices.Biometrics · 2025Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
It is of great interest for a biomedical analyst or an investigator to correctly model the CD4 cell count or disease biomarkers of a patient in the presence of covariates or factors determining the disease progression over time. The Poisson mixed-effects models (PMM) can be an appropriate choice for repeated count data. However, this model is not realistic because of the restriction that the mean and variance are equal. Therefore, the PMM is replaced by the negative binomial mixed-effects model (NBMM). The later model effectively manages the over-dispersion of the longitudinal data. We evaluate and compare the proposed models and their application to the number of CD4 cells of HIV-Infected patients recruited in the CAPRISA 002 Acute Infection Study. The results display that the NBMM has appropriate properties and outperforms the PMM in terms of handling over-dispersion of the data. Multiple imputation techniques are also used to handle missing values in the dataset to get valid inferences for parameter estimates. In addition, the results imply that the effect of baseline BMI, HAART initiation, baseline viral load, and the number of sexual partners were significantly associated with the patient's CD4 count in both fitted models. Comparison, discussion, and conclusion of the results of the fitted models complete the study.
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