Plain Language Summary

This research addresses the challenges faced in evaluating new treatments for Alzheimer’s disease, particularly concerning the differences in health economic models used to predict outcomes for people with mild cognitive impairment due to Alzheimer’s disease. As new treatments emerge, payers and healthcare providers need reliable data to make informed choices about accessibility and reimbursement. However, various models can yield different predictions, which creates uncertainty for stakeholders relying on this information.

During a recent workshop, 9 research groups compared their health economic models based on a standard scenario involving a hypothetical treatment for mild cognitive impairment. The results showed significant variations in predictions of how long patients would remain in the mild cognitive impairment stage and the associated costs and quality-adjusted life years (QALYs). For instance, the time predicted for patients to stay in mild cognitive impairment ranged between 2.6 to 5.2 years in the control group, while the difference between intervention and control strategies was between 0.1 to 1.0 years. Similarly, QALY gains and incremental costs also varied significantly across models, demonstrating how different modeling approaches can lead to substantially different results.

Several factors contributed to this variation. Different outcome measures selected from clinical trials impacted the reported effectiveness of treatments, which in turn influenced predictions. The choice of how to analyze data, including methods for tracking disease progression and the assumptions about treatment effects over time, also played a critical role. For example, the way models handled treatment discontinuation and waning effects showed marked differences.

To support the credibility and transparency of these models, researchers recommend several strategies. First, it is essential to conduct sensitivity analyses that address the assumptions made about treatment effectiveness and outcome measures. Second, a standardized reporting format for model predictions would improve clarity and facilitate cross-comparison. Lastly, developing long-term registries to gather real-world data on disease progression could help bridge the gap between short-term clinical trial results and long-term health outcomes.

Overall, this research underlines the importance of consistent methodologies in health economic modeling for Alzheimer’s disease to support better decision making for patients, healthcare providers, and policy makers. By addressing the noted variations and implementing the proposed recommendations, stakeholders can foster more reliable evaluations of new treatments for Alzheimer’s disease, ultimately leading to improved care for those affected by this condition.

^{Note: This content was created with assistance from artificial intelligence (AI) and has been reviewed and edited by ISPOR staff. For more information or for inquiries on ISPOR’s AI policy, click here or contact us at info@ispor.org.}