Meta-analysis of economic evaluation studies: data harmonisation and methodological issues

Bhavani Shankara Bagepally,Usa Chaikledkaew,Nathorn Chaiyakunapruk,John Attia,Ammarin Thakkinstian

doi:10.1186/s12913-022-07595-1

Abstract

BackgroundIn the context of ever-growing health expenditure and limited resources, economic evaluations aid in making evidence-informed policy decisions. Cost-utility analysis (CUA) is often used, and CUA data synthesis is also desirable, but methodological issues are challenged. Hence, we aim to provide a step-by-step process to prepare the CUA data for meta-analysis.MethodsData harmonisation methods were constructed specifically considering CUA methodology, including inconsistent reports, economic parameters, heterogeneity (i.e., country’s income, time horizon, perspective, modelling approaches, currency, willingness to pay). An incremental net benefit (INB) and its variance were estimated and pooled across studies using a basic meta-analysis by COMER.ResultsFive scenarios show how to obtain INB and variance with various reported data: Study reports the mean and variance (Scenario 1) or 95% confidence interval (Scenario 2) of ΔC, ΔE, and ICER for INB/variance calculations. Scenario 3: ΔC, ΔE, and variances are available, but not for the ICER; a Monte Carlo was used to simulate ΔC and ΔE data, variance and covariance can be then estimated leading INB calculation. Scenario-4: Only the CE plane was available, ΔC and ΔE data can be extracted; means of ΔC, ΔE, and variance/covariance can be estimated accordingly, leading to INB/variance estimates. Scenario-5: Only mean cost/outcomes and ICER are available but not for variance and the CE-plane. A variance INB can be borrowed from other studies which are similar characteristics, including country income, ICERs, intervention-comparator, time period, country region, and model type and inputs (i.e., discounting, time horizon).ConclusionOut data harmonisation and meta-analytic methods should be useful for researchers for the synthesis of economic evidence to aid policymakers in decision making.

Highlights

In the context of ever-growing health expenditure and limited resources, economic evaluations aid in making evidence-informed policy decisions
The costs are usually measured in a specific country currency, while the health benefit is usually measured as a quality adjusted life year (QALY), i.e., the product of years lived and health utility score ranging from 0 to 1, or disability adjusted life years (DALY) [2, 3]
Basic methods of MA in evaluation studies (EES), including identifying and selecting relevant studies, are similar to other systematic reviews and MAs [5, 17] and should follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [18] guideline when reported. This methodological study was a part of previous MAs, in which some additional specific issues apply to EESs are as follows [19,20,21,22]; the relevant protocol was registered in Prospero (PROSPERO 2018 CRD42018105193)

Summary

Introduction

In the context of ever-growing health expenditure and limited resources, economic evaluations aid in making evidence-informed policy decisions. The guidelines from Joanna Briggs Institute, Cochrane group [5, 6], mainly provide guidelines towards qualitative synthesis or only systematic review of all sorts of economic evaluation (e.g., cost–benefit analysis, cost minimisation analysis, cost-effective analysis, cost-utility analysis) [7,8,9,10,11]. These guidelines have a limited focus on data extraction and data harmonisation process to prepare the data for the meta-analysis [7,8,9,10,11]

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