Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA (DDK).
Pharmaceutical Outcomes Research and Policy Program, Department of Pharmacy, and the Departments of Health Services and Economics University of Washington, Seattle, WA, USA (AB).
Med Decis Making. 2017 Nov;37(8):930-941. doi: 10.1177/0272989X17702379. Epub 2017 Apr 25.
Cost-effectiveness analysis (CEA) methods fail to acknowledge that where cost-effectiveness differs across subgroups, there may be differential adoption of technology. Also, current CEA methods are not amenable to incorporating the impact of policy alternatives that potentially influence the adoption behavior. Unless CEA methods are extended to allow for a comparison of policies rather than simply treatments, their usefulness to decision makers may be limited.
We conceptualize new metrics, which estimate the realized value of technology from policy alternatives, through introducing subgroup-specific adoption parameters into existing metrics, incremental cost-effectiveness ratios (ICERs) and Incremental Net Monetary Benefits (NMBs). We also provide the Loss with respect to Efficient Diffusion (LED) metrics, which link with existing value of information metrics but take a policy evaluation perspective. We illustrate these metrics using policies on treatment with combination therapy with a statin plus a fibrate v. statin monotherapy for patients with diabetes and mixed dyslipidemia.
Under the traditional approach, the population-level ICER of combination v. monotherapy was $46,000/QALY. However, after accounting for differential rates of adoption of the combination therapy (7.2% among males and 4.3% among females), the modified ICER was $41,733/QALY, due to the higher rate of adoption in the more cost-effective subgroup (male). The LED metrics showed that an education program to increase the uptake of combination therapy among males would provide the largest economic returns due to the significant underutilization of the combination therapy among males under the current policy.
This framework may have the potential to improve the decision-making process by producing metrics that are better aligned with the specific policy decisions under consideration for a specific technology.
成本效益分析（CEA）方法未能认识到，在成本效益在亚组之间存在差异的情况下，技术的采用可能存在差异。此外，当前的 CEA 方法不适合纳入可能影响采用行为的政策替代方案的影响。除非 CEA 方法得到扩展，以允许比较政策而不仅仅是治疗方法，否则它们对决策者的有用性可能有限。
我们通过将亚组特定的采用参数引入现有的衡量标准，即增量成本效益比（ICER）和增量净货币效益（NMB），来概念化新的衡量标准，这些衡量标准可以估计政策替代方案带来的技术实现价值。我们还提供了与现有信息价值衡量标准相关联但采用政策评估视角的与有效扩散相关的损失（LED）衡量标准。我们使用针对糖尿病和混合血脂异常患者使用他汀类药物联合贝特类药物治疗与他汀类药物单药治疗的政策来说明这些衡量标准。
在传统方法下，联合治疗与单药治疗的人群水平 ICER 为 46,000 美元/QALY。然而，在考虑到联合治疗采用率的差异（男性为 7.2%，女性为 4.3%）后，由于更具成本效益的亚组（男性）采用率更高，修正后的 ICER 为 41,733 美元/QALY。LED 衡量标准表明，一项旨在提高男性对联合治疗的接受度的教育计划将提供最大的经济回报，因为在当前政策下，男性对联合治疗的利用率显著不足。
该框架有可能通过生成与特定技术下正在考虑的特定政策决策更一致的衡量标准来改善决策过程。
