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dc.contributor.authorYirga, Ashenafi A
dc.contributor.authorMelesse, Sileshi F
dc.contributor.authorMwambi, Henry G
dc.contributor.authorAyele, Dawit G
dc.date.accessioned2020-10-20T14:09:45Z
dc.date.available2020-10-20T14:09:45Z
dc.date.issued2020-10-07
dc.identifier.urihttp://hdl.handle.net/10713/13907
dc.description.abstractIt 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.en_US
dc.description.sponsorshipCAPRISA is funded by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes for Health (NIH), and U.S. Department of Health and Human Services (Grant: AI51794). The authors would also like to thank Dr. Nonhlanhla Yende Zuma (Head of Biostatistics unit at CAPRISA) for her cooperation, assistance, and technical support.en_US
dc.description.urihttps://doi.org/10.1038/s41598-020-73883-7en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofScientific reportsen_US
dc.subjectnegative binomial mixed-effects modelen_US
dc.subjectover-dispersionen_US
dc.subject.meshBiomarkersen_US
dc.subject.meshCD4 Lymphocyte Counten_US
dc.subject.meshHIV Infectionsen_US
dc.titleNegative binomial mixed models for analyzing longitudinal CD4 count data.en_US
dc.typeArticleen_US
dc.typeOtheren_US
dc.identifier.doi10.1038/s41598-020-73883-7
dc.identifier.pmid33028929
dc.source.volume10
dc.source.issue1
dc.source.beginpage16742
dc.source.endpage
dc.source.countryUnited States
dc.source.countryUnited Kingdom
dc.source.countryEngland


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