INNOVATIONS in pharmacy

Cost-Effectiveness of a Hypothetical Gene Therapy for Alzheimer's Disease: A Markov Simulation Analysis

Thuy Chinh Kieu

University of Wisconsin-Madison

Kevin Look

University of Wisconsin-Madison

DOI: https://doi.org/10.24926/iip.v14i3.5500

Keywords: cost effectiveness, Markov model, Alzheimer's disease, gene therapy

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Abstract

Background: Alzheimer’s disease is a prevalent neurodegenerative condition causing significant health and economic burden. With limited therapeutic options, clinical trials have been investigating Alzheimer’s disease treatment using more novel approaches, including gene therapy. However, there is limited evidence on the cost-effectiveness of such treatments.

Objectives: This research aims to explore the cost-effectiveness of a hypothetical gene therapy for patients with Alzheimer’s disease at varying degrees of severity.

Methods: A Markov model with a 20-year time horizon was constructed for simulated cohorts with mild cognitive impairment due to Alzheimer’s disease, assigned to receive either standard of care or a one-time gene therapy administration. Varying costs of care due to disease severity and treatment efficacy were utilized to determine the effect of those variables at different willingness-to-pay thresholds. 

Results: Under the initial assumption that the hypothetical gene therapy grants a 30% risk reduction in disease progression and entry into institutional care, the maximum cost-effective price for gene therapy is $141,126 per treatment using the threshold of $150,000 per quality-adjusted life year (QALY). By increasing the treatment effectiveness to 50%, cost-effective price nearly doubled at each willingness-to-pay threshold (e.g., $260,902 at the $150,000/QALY threshold).

Conclusion: Despite being cost-effective at a very high price, the hypothetical gene therapy for AD would still need to be priced considerably lower than other approved gene therapies on the market. Thus, a comprehensive pharmacoeconomic assessment remains critical in pricing innovative therapy and determining coverage for patients in need.