# Meta‐Analysis of Cost‐Effectiveness

**Authors:** Heejung Bang, Hongwei Zhao

PMC · DOI: 10.1002/sim.70352 · 2026-03-18

## TL;DR

This paper introduces new methods for combining cost-effectiveness data in systematic reviews, aiming to simplify a complex analysis process.

## Contribution

The paper proposes simple meta-analytic methods for cost-effectiveness, addressing a gap in current statistical approaches.

## Key findings

- Current statistical methods for cost-effectiveness meta-analysis are limited and not widely used.
- The proposed methods are illustrated with examples from wound interventions and mental illness reviews.
- The methods aim to provide a foundation for future research in cost-effectiveness meta-analysis.

## Abstract

Systematic review and meta‐analysis are widely accepted approaches for evaluating treatment effectiveness. Meta‐analysis generally addresses statistical aspects of systematic reviews, such as the pooling of treatment effect sizes, assessment of heterogeneity, and statistical inference. To complement treatment effectiveness, cost‐effectiveness is often conducted to encompass both clinical and economic perspectives. However, there are few statistical methods proposed for meta‐analyses of cost‐effectiveness, and none is used widely. In fact, meta‐analysis is currently not encouraged for cost‐effectiveness due to methodological and statistical complexities. In this paper, we propose simple meta‐analytic methods for cost‐effectiveness, which may serve as a starting point for future work. We illustrate the methods using two examples from systematic reviews on wound interventions and mental illness.

## Full-text entities

- **Diseases:** CEA (MESH:D065606), sickness absence (MESH:D004832), Mental Illness (MESH:D001523), ulcer (MESH:D014456), depression (MESH:D003866)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12999371/full.md

---
Source: https://tomesphere.com/paper/PMC12999371