ABSTRACT
Background
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.
Methods
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.
Results
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.
Conclusion
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.