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Negative binomial mixed models for analyzing longitudinal CD4 count data
Ashenafi A Yirga1, Sileshi F Melesse2, Henry G Mwambi2
1School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa. ashu3argaw@gmail.com.
Scientific Reports
|October 8, 2020
Summary
The negative binomial mixed-effects model (NBMM) better handles over-dispersion in longitudinal CD4 cell count data compared to Poisson mixed-effects models (PMM). This advanced modeling improves understanding of HIV disease progression factors.
Area of Science:
- Biostatistics
- Epidemiology
- Immunology
Background:
- Accurate modeling of longitudinal CD4 cell counts is crucial for understanding HIV disease progression.
- Poisson mixed-effects models (PMM) are limited by their equal mean and variance assumption, which is often violated in biological data.
- Over-dispersion, where variance exceeds the mean, is common in count data, necessitating more flexible models.
Purpose of the Study:
- To compare the performance of negative binomial mixed-effects models (NBMM) against Poisson mixed-effects models (PMM) for modeling longitudinal CD4 cell counts in HIV-infected patients.
- To identify key covariates associated with CD4 cell count changes over time in the context of HIV infection.
- To assess the utility of multiple imputation techniques for handling missing data in this longitudinal study.
Main Methods:
- Longitudinal data analysis using Poisson mixed-effects models (PMM) and negative binomial mixed-effects models (NBMM).
- Application of models to CD4 cell count data from the CAPRISA 002 Acute Infection Study.
- Utilized multiple imputation to address missing values in the dataset.
Main Results:
- The negative binomial mixed-effects model (NBMM) demonstrated superior performance in handling over-dispersion compared to the Poisson mixed-effects model (PMM).
- Multiple imputation effectively handled missing data, enabling valid parameter estimation.
- Baseline BMI, HAART initiation, baseline viral load, and number of sexual partners were significantly associated with CD4 cell counts.
Conclusions:
- NBMM is a more appropriate statistical tool than PMM for analyzing longitudinal count data with over-dispersion, such as CD4 cell counts in HIV-infected individuals.
- Accurate modeling of CD4 cell counts is essential for identifying factors influencing HIV disease progression.
- The study highlights the importance of considering factors like BMI, HAART, viral load, and sexual behavior in managing HIV infection.
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