Statin's cost-effectiveness: a Canadian analysis of commonly prescribed generic and brand name statins.
Bibliographic record
Abstract
BACKGROUND: Generic statins may be considered as a compelling treatment option for managing dyslipidemia, due to their reduced cost, compared to their brand name equivalent. However, further assessment is needed to determine whether using a particular generic statin is more cost-effective relative to other brand-name statins. OBJECTIVE: The purpose of this study is to compare the cost-effectiveness of the most commonly prescribed statins in Canada with respect to 1) lowering low-density lipoprotein cholesterol level (LDL-C) and 2) achieving National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) LDL-C goal. METHODS: The study was conducted from the perspective of Canadian payers over a 1-year time horizon. Clinical data were obtained from the STELLAR trial (n=2268) in which patients received fixed doses of rosuvastatin, atorvastatin, simvastatin and pravastatin. Brand and generic drug costs were based on wholesale acquisition costs. Relative cost-effectiveness was assessed using the net monetary benefit approach (NMB), which allows probabilistic cost-effectiveness comparison of the various treatment options over a wide range of willingness-to-pay (WTP) values for a unit of clinical effect. RESULTS: Rosuvastatin 10mg was the most cost-effective statin over the largest range of WTP values. Pravastatin 10mg was cost-effective when the clinical outcomes had little or no monetary value. Rosuvastatin 20 mg was more cost-effective at the highest end of the WTP spectrum. CONCLUSION: The result of this analysis provides evidence that prescribing generic statins in Canada does not necessarily translate into the most cost-effective option for treating dyslipidemia; especially as the monetary value of 1% decrease in LDL-C or patients achieving NCEP ATP III target increases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".