Model to assess the cost-effectiveness of new treatments for depression
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to develop a model to assess the cost-effectiveness of a new treatment for patients with depression. METHODS: A Markov simulation model was constructed to evaluate standard care for depression as performed in clinical practice compared with a new treatment for depression. Costs and effects were estimated for time horizons of 6 months to 5 years. A naturalistic longitudinal observational study provided data on costs, quality of life, and transition probabilities. Data on long-term consequences of depression and mortality risks were collected from the literature. Cost-effectiveness was quantified as quality-adjusted life-years (QALYs) gained from the new treatment compared with standard care, and the societal perspective was taken. Probabilistic analyses were conducted to present the uncertainty in the results, and sensitivity analyses were conducted on key parameters used in the model. RESULTS: Compared with standard care, the new hypothetical therapy was predicted to substantially decrease costs and was also associated with gains in QALYs. With an improved treatment effect of 50 percent on achieving full remission, the net cost savings were 20,000 Swedish kronor over a 5-year follow-up time, given equal costs of treatments. Patients gained .073 QALYs over 5 years. The results are sensitive to changes in assigned treatment effects. CONCLUSIONS: The present study provides a new model for assessing the cost-effectiveness of treatments for depression by incorporating full remission as the treatment goal and QALYs as the primary outcome measure. Moreover, we show the usefulness of naturalistic real-life data on costs and quality of life and transition probabilities when modeling the disease over time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".