Cost effectiveness of duloxetine compared with venlafaxine‐XR in the treatment of major depressive disorder
Bibliographic record
Abstract
PURPOSE: To determine the cost effectiveness of duloxetine, a new serotonin norepinephrine reuptake inhibitor, when compared with venlafaxine-XR in treating major depressive disorder. METHODS: A cost effectiveness analysis, using a decision tree modelled outpatient treatment over 6 months. Analytic perspectives were those of society (all direct and indirect costs) and the Ministry of Health of Ontario (MoH) as payer for all direct costs. Rates of success and dropouts were obtained from a meta-analysis of randomized placebo-controlled trials. Costs were taken from standard lists, adjusted to 2005 Canadian dollars; discounting was not applied. One-way sensitivity analyses were performed on monthly acquisition costs and success rates; Monte-Carlo analysis examined all parameters over 10000 iterations. RESULTS: From both perspectives, outcomes all numerically favoured venlafaxine-XR (Expected success = 53% and 57%; symptom-free days [SFDs] = 52.72 and 57.03 for duloxetine and venlafaxine-XR, respectively). Total expected costs/patient treated were, Can dollar 7081 and Can dollar 6551 (MoH), Can dollar 20987 and Can dollar 19 997 (societal perspective), for duloxetine and venlafaxine-XR, respectively. Expected costs/SFD were Can dollar134 and Can dollar 115 (MoH) and Can dollar 398 and Can dollar 351 (societal viewpoint) for duloxetine and venlafaxine-XR, respectively. Although results were sensitive to changes in success rate within the 95% CI, Monte-Carlo analyses using the ICER (incremental cost effectiveness ratio) as outcome found venlafaxine-XR was dominant in approximately 78% of scenarios in both perspectives. CONCLUSIONS: Differences in pharmacoeconomic outcomes found were modest, but in all cases, favoured venlafaxine-XR over duloxetine. Therefore, a possible advantage may exist at the population level in the treatment of major depressive disorder in Canada. Ultimately, a head to head study of the two drugs would be needed to confirm these findings.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".