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

Background In 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. Methods Data 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. Results Five 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 Delta C, Delta E, and ICER for INB/variance calculations. Scenario 3: Delta C, Delta E, and variances are available, but not for the ICER; a Monte Carlo was used to simulate Delta C and Delta E data, variance and covariance can be then estimated leading INB calculation. Scenario-4: Only the CE plane was available, Delta C and Delta E data can be extracted; means of Delta C, Delta E, 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). Conclusion Out data harmonisation and meta-analytic methods should be useful for researchers for the synthesis of economic evidence to aid policymakers in decision making.

Automated summary

This study provides a step-by-step process to prepare cost-utility analysis (CUA) data for meta-analysis. The results show how to obtain incremental net benefit (INB) and variance data in different scenarios.