The Cost Effectiveness of Lisdexamfetamine Dimesylate for the Treatment of Binge Eating Disorder in the USA
Bibliographic record
Abstract
BACKGROUND: Lisdexamfetamine dimesylate (LDX) demonstrated efficacy in terms of reduced binge eating days per week in adults with binge eating disorder (BED) in two randomized clinical trials (RCTs). OBJECTIVE: The objective of this study was to evaluate the cost effectiveness of LDX versus no pharmacotherapy (NPT) in adults with BED from a USA healthcare payer's perspective. STUDY DESIGN AND METHODS: A decision-analytic Markov cohort model was developed using 1-week cycles and a 52-week time horizon. Markov health states were defined based upon the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria of BED. Model parameter estimates were obtained from RCTs, a survey, and literature. The primary outcome was incremental cost-effectiveness ratio (ICER). The analysis assumed a 12-week course of treatment, based upon RCTs' treatment duration. One-way deterministic and probabilistic sensitivity analyses were conducted to assess the robustness of the results. RESULTS: Patients on LDX therapy gained 0.006 quality-adjusted life years (QALY) compared to patients on the NPT arm, while the average total cost was US$175 higher for LDX therapy. The estimated ICER for LDX compared with NPT was US$27,618 per QALY, which was shown to be cost effective given a willingness-to-pay threshold of US$50,000. CONCLUSIONS: Treatment of BED with LDX showed increase in QALYs at an acceptable cost and is considered to be cost effective at the commonly used willingness-to-pay threshold in the USA. Based on the available evidence, the current model focused on short-term benefits only. There is a need to generate additional scientific evidence supporting long-term benefits of LDX therapy for BED.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".