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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".