OBJECTIVES:

Cost-effectiveness analyses commonly use population averages, which can mask key differences across subgroups, potentially resulting in suboptimal resource allocation. This study sought to identify recent advances in methods for acknowledging patient heterogeneity in economic evaluations and to provide an overview of these methods.

METHODS:

A literature search was conducted in April 2022, using Econlit, Embase and MEDLINE databases, to identify literature published since 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to acknowledging patient heterogeneity within a full economic evaluation. Citation pearl growing supplemented the database searches. Two stages of screening were applied and author consensus was used to determine the studies to be included. Extracted data captured relevant study design (model or trial), phase of the economic evaluation, methods, case studies presented, and strengths and limitations.

RESULTS:

Database searches identified 1,403 records after duplicate removal, with 14 studies included in the review. Four studies were added following citation pearl growing. Included methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields (e.g., machine learning and causal forests). Commonly noted challenges associated with acknowledging heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). Studies also discussed a need to consider ethical issues and feasibility of implementation.

CONCLUSIONS:

There is a growing range of methods available to acknowledge patient heterogeneity in economic evaluation. Relevant methods differ according to disease area, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with acknowledging patient heterogeneity to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.