Modeling HIV/AIDS Progression: A Comparative Analysis of the 3-Parameter Weibull, AFT, and Cox Proportional Hazards Models
Bibliographic record
Abstract
This study models HIV/AIDS progression using the 3-Parameter Weibull Model and its adaptations, specifically the Accelerated Failure Time (AFT) Weibull and Cox Proportional Hazards (PH) models, to compare outcomes across age groups (20-30, 30-40, 40-50, 50-60, and over 60) and genders. Key performance metrics included Z-statistics, P-values, AD values, and standard errors to evaluate model fit and accuracy. The 3-Parameter Weibull model’s flexibility for time-varying hazards makes it well-suited for chronic conditions influenced by antiretroviral therapy (ART) and demographic factors. Results showed that the AFT model captured ART effects effectively in the 50-60 age group, particularly among males, while its predictive power decreased for younger cohorts, where ART’s impact was less pronounced. The Cox PH model, although interpretable, struggled in dynamic hazard rate scenarios, performing moderately in stable age groups but limited in detecting ART effects overall. The 3-Parameter Weibull model showed a strong fit in the 40-50 and 50-60 groups, with significant metrics affirming ART’s impact on survival, though predictive precision declined for those over 60. These findings highlight the complementary strengths of the AFT and 3- Parameter Weibull models, suggesting their integrated use can enhance state-specific modeling of HIV/AIDS progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".