The Potential Economic 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 disease that affects memory, thinking, and behavior and places a substantial economic burden on caregivers and healthcare systems. This early-phase study aimed to model lecanemab, a humanized monoclonal antibody targeting amyloid protofibrils, for patients with early AD, and estimate the potential value-based price (VBP) of lecanemab + standard of care (SoC) compared to SoC alone given an expected product profile of lecanemab informed by data from a phase II trial from payer and societal perspectives using a broad range of willingness-to-pay (WTP) thresholds in the USA. METHODS: A disease simulation model was used to capture how key AD pathology components relate to the clinical and economic presentation of AD. The effects of disease modification and early intervention on disease progression were simulated on the basis of BAN2401-G000-201 trial data as well as published literature. Model outcomes included patient and caregiver quality-adjusted life years (QALYs), total life years, and total care costs including direct medical and non-medical costs for healthcare resource use and indirect costs for caregiving over a lifetime horizon. RESULTS: Lecanemab + SoC was predicted to result in a gain of 0.61 QALYs (societal, 0.64) and a $8707 decrease in total non-treatment costs (societal, $11,214) vs. SoC alone for patients with early AD. For a WTP threshold range of $50,000 to $200,000 per QALY gained, the potential annual VBP of lecanemab was estimated at $9249 (societal, $10,400) to $35,605 (societal, $38,053), respectively. Other patient subsets, treatment stopping rules, and dosing regimens were used to assess the sensitivity of the VBP estimates. CONCLUSION: The early model predicted that lecanemab would potentially improve long-term health outcomes and reduce formal and informal care costs, resulting in a range of VBPs that reflect the value of lecanemab to society.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".