Cost-Effectiveness Analysis of Daridorexant for the Pharmacological Treatment of Chronic Insomnia Disorder in Adults
Bibliographic record
Abstract
OBJECTIVE: Daridorexant 50 mg is recommended for treating chronic insomnia in England, Wales (NICE, 2023) and Scotland (Scottish Medicines Consortium, 2024). This study examines the model and cost-effectiveness profile that led to these positive reimbursements. METHODS: The cost-effectiveness model integrated data from daridorexant 50 mg phase III trials (studies 301 and 303) and the National Health and Wellness Survey (NHWS). Clinical parameters were the Insomnia Severity Index (ISI) score and adverse events. Using the NHWS, ISI data were mapped to utility, healthcare resource use, and work productivity. Daridorexant 50 mg was priced at £1.40/day. The base-case time horizon was 1 year. A lifetime model explored long-term effects. Parameters, data inputs, structural uncertainty, and alternative scenarios are all presented. RESULTS: In the 12-months model compared with placebo, daridorexant was estimated to have an incremental cost of £389 and generate an additional 0.024 quality-adjusted life-years (QALYs), resulting in an incremental cost-effectiveness ratio (ICER) of £16,300 per additional QALY from a health service perspective. Due to selective attrition, the ICER improved to £9580 per QALY for those continuing treatment for >12 months. Adopting a societal productivity perspective, daridorexant was estimated to offer £596 (£330-£896) total productivity savings versus £411/year in treatment costs, leading to a situation of dominance. Lifetime modeling improved the long-term cost effectiveness of daridorexant under the assumption that any waning of treatment effect led to further dropout. CONCLUSION: Daridorexant 50 mg is estimated to be a cost-effective pharmacological treatment for chronic insomnia disorder in adult patients.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".