Subgroups and heterogeneity in cost-effectiveness analysis.

Area of Science:

• Health economics
• Decision analysis
• Health technology assessment

Background:

• The National Institute for Health and Clinical Excellence (NICE) in the UK NHS evaluates health technologies based on cost-effectiveness.
• Maximizing population health gain within a fixed budget is a key objective for healthcare systems.
• Patient characteristics can influence disease prognosis, treatment costs, and effectiveness, leading to variations in cost-effectiveness.

Purpose of the Study:

• To explore the methodological and equity considerations of using subgroup analyses in cost-effectiveness analysis (CEA).
• To inform NICE's methods guidance updates regarding heterogeneity in CEA.
• To ensure healthcare resource allocation reflects patient-specific variations.

Main Methods:

• Review of literature on subgroup analysis in CEA.
• Identification of sources of heterogeneity beyond relative treatment effects (e.g., baseline event rates).
• Consideration of methods for estimating heterogeneous model parameters and quantifying uncertainty.

Main Results:

• Subgroup analysis in CEA requires defining various sources of heterogeneity.
• Estimating parameters and quantifying uncertainty in heterogeneous models are critical methodological challenges.
• The equity implications of using subgroup analyses for decision-making require careful consideration.

Conclusions:

• NICE and similar bodies need to account for patient heterogeneity in CEA to optimize health outcomes.
• Methodological advancements are needed to address parameter estimation, uncertainty quantification, and equity in subgroup CEA.
• Updating methods guidance is essential for robust and equitable health technology assessment.