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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...

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Autores principales: Bagepally, Bhavani Shankara, Chaikledkaew, Usa, Chaiyakunapruk, Nathorn, Attia, John, Thakkinstian, Ammarin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8845252/
https://www.ncbi.nlm.nih.gov/pubmed/35168619
http://dx.doi.org/10.1186/s12913-022-07595-1
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author Bagepally, Bhavani Shankara
Chaikledkaew, Usa
Chaiyakunapruk, Nathorn
Attia, John
Thakkinstian, Ammarin
author_facet Bagepally, Bhavani Shankara
Chaikledkaew, Usa
Chaiyakunapruk, Nathorn
Attia, John
Thakkinstian, Ammarin
author_sort Bagepally, Bhavani Shankara
collection PubMed
description 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 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). 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-07595-1.
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spelling pubmed-88452522022-02-16 Meta-analysis of economic evaluation studies: data harmonisation and methodological issues Bagepally, Bhavani Shankara Chaikledkaew, Usa Chaiyakunapruk, Nathorn Attia, John Thakkinstian, Ammarin BMC Health Serv Res Research 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 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). 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-07595-1. BioMed Central 2022-02-15 /pmc/articles/PMC8845252/ /pubmed/35168619 http://dx.doi.org/10.1186/s12913-022-07595-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bagepally, Bhavani Shankara
Chaikledkaew, Usa
Chaiyakunapruk, Nathorn
Attia, John
Thakkinstian, Ammarin
Meta-analysis of economic evaluation studies: data harmonisation and methodological issues
title Meta-analysis of economic evaluation studies: data harmonisation and methodological issues
title_full Meta-analysis of economic evaluation studies: data harmonisation and methodological issues
title_fullStr Meta-analysis of economic evaluation studies: data harmonisation and methodological issues
title_full_unstemmed Meta-analysis of economic evaluation studies: data harmonisation and methodological issues
title_short Meta-analysis of economic evaluation studies: data harmonisation and methodological issues
title_sort meta-analysis of economic evaluation studies: data harmonisation and methodological issues
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8845252/
https://www.ncbi.nlm.nih.gov/pubmed/35168619
http://dx.doi.org/10.1186/s12913-022-07595-1
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