成本效益
医学
成本效益分析
检查表
质量调整寿命年
成本效益分析
风险分析(工程)
心理学
生物
生态学
认知心理学
作者
Sumaya Abuloha,Shu Niu,Darlene Adirika,Benjamin P. Harvey,Mikael Svensson
出处
期刊:Human Gene Therapy
[Mary Ann Liebert]
日期:2024-03-25
卷期号:35 (11-12): 365-373
被引量:3
摘要
Cell and gene therapy innovations have provided several significant breakthroughs in recent years. However, cell and gene therapies often come with a high upfront cost, raising questions about patient access, affordability, and long-term value. This study reviewed cost-effectiveness analysis studies that have attempted to assess the long-term value of FDA-approved cell and gene therapies. Two reviewers independently searched the Tufts Medical Center Cost-Effectiveness Analysis Registry to identify all studies for FDA-approved cell and gene therapies per January 2023. A data extraction template was used to summarize the evidence in terms of the incremental cost-effectiveness ratio expressed as the cost per Quality-Adjusted Life-Year (QALY) and essential modeling assumptions, combined with a template to extract the adherence to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. The review identified 26 CEA studies for seven cell and gene therapies. Around half of the base-case cost-effectiveness results indicated that the cost per QALY was below $100,000-$150,000, often used as a threshold for reasonable cost-effectiveness in the US. However, the results varied substantially across studies for the same treatment, ranging from being considered very cost-effective to far from cost-effective. Most models were based on data from single-arm trials with relatively short follow-ups, and different long-term extrapolations between studies caused large differences in the modeled cost-effectiveness results. In sum, this review showed that despite the high upfront costs, many cell and gene therapies have cost-effectiveness evidence that can support long-term value. Nonetheless, substantial uncertainty regarding long-term value exists because so much of the modeling results are driven by uncertain extrapolations beyond the clinical trial data.
科研通智能强力驱动
Strongly Powered by AbleSci AI