Cost-effectiveness of combination therapy with etravirine in treatment-experienced adults with HIV-1 infection
Bibliographic record
Abstract
OBJECTIVE: To assess the cost-effectiveness of etravirine (INTELENCE), a novel nonnucleoside reverse transcriptase inhibitor, used in combination with a background regimen that included darunavir/ritonavir, from a Canadian Provincial Ministry of Health perspective. DESIGN: A Markov model with a 3-month cycle time and six health states based on CD4 cell count ranges was developed to follow a hypothetical cohort of treatment-experienced adults with HIV-1 infection through initial and subsequent treatment regimens. METHODS: Costs (in 2009 Canadian dollars), utilities, and HIV-related mortality data for each health state as well as non-HIV-related mortality data were estimated from Canadian sources and published literature. Transition probabilities between health states and first-year hospitalization and mortality rates were derived from clinical trial data. Incremental 1-year costs per additional adult with viral load less than 50 copies/ml at 48 weeks and incremental lifetime costs per quality-adjusted life-year (QALY) gained were estimated using a 5% discount rate. Sensitivity and variability analyses and model validation were performed. RESULTS: Etravirine was associated with an increased probability of achieving less than 50 copies/ml at 48 weeks of 0.205 and an estimated gain of 0.66 discounted (1.48 undiscounted) QALYs over a lifetime. The incremental 1-year cost per additional person with viral load less than 50 copies/ml was $23,862. The lifetime incremental cost per QALY gained was $49,120. For the uncertainty ranges and variability scenarios tested for the lifetime horizon, the cost-effectiveness ratio was between $28,859 and 66,249. CONCLUSION: When compared with optimized standard of care including darunavir/ritonavir, adding etravirine represents a cost-effective option for treatment-experienced adults in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".