Cost-effectiveness analysis of rosuvastatin vs generic atorvastatin in Spain
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to carry out a long-term cost-effectiveness analysis of rosuvastatin compared with generic atorvastatin in the treatment of patients at high cardiovascular (CV) risk (≥ 5% Systematic COronary Risk Evaluation [SCORE]) and patients with prior cardiovascular disease (CVD) in Spain. METHODS: The efficacy data from the Statin Therapies for Elevated Lipid Levels compared Across doses to Rosuvastatin (STELLAR) study were used to simulate achievement of low-density lipoprotein cholesterol targets with different doses of rosuvastatin and generic atorvastatin for an initial period of 1 year. A Markov model was used to estimate the number of CV complications, quality-adjusted life years (QALYs), and healthcare costs (lipid-lowering treatment and CV events) for up to 20 years after initial treatment. The analysis was carried out from the perspective of the Spanish National Health System, with costs (in year 2010 euros) and effects being discounted at 3% per year. RESULTS: Compared with generic atorvastatin, rosuvastatin was cost-effective (cost per QALY gained of less than €30,000) for the primary prevention of CV events in high-risk patients in most sub-groups analyzed. In patients with prior CVD, rosuvastatin was cost-effective in all sub-groups of men and most sub-groups of women. Key limitations of this study were the need to extrapolate data from a single trial to long-term modeled outcomes and the absence of other treatment options in the analysis. CONCLUSIONS: For the treatment of dyslipidemic patients with high CV risk, rosuvastatin is more effective than generic atorvastatin in terms of survival and quality-of-life adjusted survival, with incremental cost-effectiveness ratios within the range generally used in Spain, in most sub-populations defined by various combinations of CV risk factors.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".