Cost-Effectiveness of Dolutegravir in HIV-1 Treatment-Naive and Treatment-Experienced Patients in Canada
Bibliographic record
Abstract
INTRODUCTION: The Antiretroviral Analysis by Monte Carlo Individual Simulation (ARAMIS) model was adapted to evaluate the cost-effectiveness of dolutegravir (DTG) in Canada in treatment-naive (TN) and treatment-experienced (TE) human immunodeficiency virus (HIV)-1 patients. METHODS: The ARAMIS-DTG model is a microsimulation model with a lifetime analytic time horizon and a monthly cycle length. Markov health states were defined by HIV health state (with or without opportunistic infection). DTG was compared to efavirenz (EFV), raltegravir (RAL), darunavir/ritonavir, rilpivirine (RPV), elvitegravir/cobicistat, atazanavir/ritonavir and lopinavir/ritonavir in TN patients and to RAL in TE patients. The initial cohort, the main efficacy data and safety data were derived from phase III clinical trials. Treatment algorithms were based on expert opinion. Costs normalized to the year 2013 included antiretroviral treatment cost, testing, adverse event, HIV and cardiovascular disease care and were derived from the literature. RESULTS: Dolutegravir was estimated to be the dominant strategy compared with all comparators in both TN and TE patients. Treatment with DTG was associated with additional quality-adjusted life-years that ranged from 0.17 (vs. RAL) to 0.47 (vs. EFV) in TN patients and was 0.60 in TE patients over a lifetime. Cost savings ranged from Can$1393 (vs. RPV) to Can$28,572 (vs. RAL) in TN patients and amounted to Can$3745 in TE patients. Sensitivity analyses demonstrated the robustness of the model. CONCLUSIONS: Dolutegravir is a dominant strategy in the management of TN and TE patients when compared to recommended comparators. This is mainly related to the high efficacy and high barrier to resistance. FUNDING: ViiV Healthcare.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".