Methods that accommodate heterogeneity in outcomes are not widely used in economic evaluation. With the growth of precision medicine (PM), where choice of treatment is informed by the molecular characteristics of the patient or disease, we expect to see greater heterogeneity in effectiveness and cost of interventions. Our objective was to compare analytical frameworks for valuing heterogeneity in economic evaluation, and consider their strengths and weaknesses for applications in PM.

We conducted a literature review to identify papers that proposed an analytical framework for economic evaluation of a health intervention, and that placed a value on heterogeneous effects. We compared the frameworks considering the purpose of the analysis, including where in the product lifecycle the framework could be used, the types of PM interventions where the framework could be applied, and its ability to address methodological challenges of evaluating PM.

Five analytical frameworks were identified: covariate adjustment methods, value of stratification, value of heterogeneity (VoH), expected value of individualized care (EVIC), and loss with respect to efficient diffusion (LED) metrics. Each framework addresses a slightly different research question, and is suited to different settings and interventions. With the exception of covariate adjustment, all focus on maximizing net benefit within certain constraints and quantify the opportunity cost of ignoring heterogeneity. Only VoH considers the relationship between heterogeneity and uncertainty, and no framework explicitly includes the cost or uncertainty associated with identifying subgroups.

The ability to value heterogeneity is a critical component of economic evaluations of PM. The choice of an appropriate analytical framework will help strengthen the quality of economic evidence available to support health technology assessment of PM technologies, informing PM adoption decisions, and supporting efficient allocation of health care resources.