The value of heterogeneity for cost-effectiveness subgroup analysis: conceptual framework and application

Area of Science:

• Health Economics
• Decision Science
• Public Health Policy

Background:

• Subgroup analysis in cost-effectiveness analysis (CEA) is crucial for personalized medicine and resource allocation in healthcare.
• Heterogeneity in patient costs and outcomes necessitates tailored decision-making frameworks.
• Existing methods often lack a structured approach to identify and value subgroup-specific information.

Purpose of the Study:

• To develop a general framework for using subgroup cost-effectiveness analysis (CEA) in collectively funded health systems.
• To guide the identification and selection of relevant patient subgroups for targeted interventions.
• To distinguish and quantify the static and dynamic value of information related to cost and outcome heterogeneity.

Main Methods:

• Development of a general framework for subgroup CEA.
• Distinguishing between the static value of existing evidence and the dynamic value of new research.
• Application of the framework to a case study of acute coronary syndrome management using 6 binary covariates.
• Calculation of expected net benefits under current and perfect information.

Main Results:

• The framework guides subgroup-specific treatment decisions and research prioritization.
• Diminishing marginal returns observed as more subgroups are considered under current information.
• Expected value of perfect information shows a decreasing trend with increased subgroup consideration.
• Case study results confirm theoretical expectations regarding subgroup value.

Conclusions:

• The proposed framework enhances decision-making in healthcare by optimizing subgroup identification and resource allocation.
• Understanding the static and dynamic value of information is key to efficient health system management.
• The study provides a practical algorithm to guide policy and research in subgroup CEA.