![]() Worldwide, 37.9 million individuals were HIV positive at the end of 2018. More than 75 million individuals have been infected with HIV, more than 32 million individuals have perished due to AIDS-related causes since the pandemic started, and 7000 new infections are reported daily. ![]() Comparison, discussion, and conclusion of the results of the fitted models complete the study.Īfter it is identified by scientists as the human immunodeficiency virus (HIV) and the cause of acquired immunodeficiency syndrome (AIDS) in 1983, HIV has spread persistently, triggering one of the most severe pandemics ever documented in human history. 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. Multiple imputation techniques are also used to handle missing values in the dataset to get valid inferences for parameter estimates. The results display that the NBMM has appropriate properties and outperforms the PMM in terms of handling over-dispersion of the 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 later model effectively manages the over-dispersion of the longitudinal data. Therefore, the PMM is replaced by the negative binomial mixed-effects model (NBMM). However, this model is not realistic because of the restriction that the mean and variance are equal. The Poisson mixed-effects models (PMM) can be an appropriate choice for repeated count data. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |