Application of Mixed Models in Analyzing CD4 Cell Counts for HIV Patients - Bermudez G, Heber E.
This study evaluates the effectiveness of mixed models in analyzing the CD4 glycoprotein count in HIV patients with advanced immune suppression, using data from the AIDS Clinical Trials Group (ACTG) study 193A in the United States. The objective was to identify factors influencing CD4 count variability and to assess the effectiveness of different antiretroviral treatments. Mixed models were applied, considering fixed and random effects for variables such as age, sex, type of treatment, and follow-up time. Several models were compared, highlighting the model with treatment-sex interaction and quadratic terms for time, which incorporated random intercepts and slopes. This model showed the best fit according to AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) criteria, effectively capturing the declining trend in CD4 counts. The analysis demonstrates that mixed models are powerful tools for understanding the complexity of longitudinal data in clinical contexts, providing a solid foundation for optimizing therapeutic decision-making in AIDS patients.