Estimated Societal Value of Lecanemab in Patients with Early Alzheimer’s Disease Using Simulation Modeling
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with memory, cognitive, and behavioral deficits, and brings significant economic burden on caregivers and healthcare systems. This study aims to estimate the long-term societal value of lecanemab plus standard of care (SoC) versus SoC alone, corresponding to a range of willingness-to-pay (WTP) thresholds based on the phase III CLARITY AD trial readouts from both the US payer and societal perspectives. METHODS: An evidence-based model was developed to simulate the effects of lecanemab on disease progression in early AD using interconnected predictive equations based on longitudinal clinical and biomarker data derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model was informed with the results of the phase III CLARITY AD trial and published literature. Key model outcomes included patient life-years (LYs), quality-adjusted life-years (QALYs), and total costs of both the direct and indirect costs of patients and caregivers over a lifetime horizon. RESULTS: Patients treated with lecanemab plus SoC gained an additional 0.62 years of life versus SoC alone (6.23 years vs. 5.61 years). The mean time on lecanemab was 3.91 years, and the treatment was associated with an increase in patient QALYs of 0.61 and an increase in total QALYs of 0.64 when both patient and caregiver utilities were considered. The model estimated that the annual value of lecanemab for the US payer perspective was US$18,709-35,678 ($19,710-37,351 for societal perspective) at the WTP threshold of $100,000-200,000 per QALY gained, respectively. Scenario analyses of patient subgroups, time horizon, input sources, treatment stopping rules, and treatment dosing were conducted to explore the impact of alternative assumptions on the model results. CONCLUSION: The economic study suggested that lecanemab plus SoC would improve health and humanistic (quality of life) outcomes and reduce economic burden for patients and caregivers in early AD.
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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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".