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Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations
Cost-effectiveness analyses (CEAs) alongside randomised controlled trials (RCTs) are increasingly designed to collect resource use and preference-based health status data for the purpose of healthcare technology assessment. However, because of the way these measures are collected, they are prone to...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5691848/ https://www.ncbi.nlm.nih.gov/pubmed/29442336 http://dx.doi.org/10.1007/s41669-017-0015-6 |
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author | Gabrio, Andrea Mason, Alexina J. Baio, Gianluca |
author_facet | Gabrio, Andrea Mason, Alexina J. Baio, Gianluca |
author_sort | Gabrio, Andrea |
collection | PubMed |
description | Cost-effectiveness analyses (CEAs) alongside randomised controlled trials (RCTs) are increasingly designed to collect resource use and preference-based health status data for the purpose of healthcare technology assessment. However, because of the way these measures are collected, they are prone to missing data, which can ultimately affect the decision of whether an intervention is good value for money. We examine how missing cost and effect outcome data are handled in RCT-based CEAs, complementing a previous review (covering 2003–2009, 88 articles) with a new systematic review (2009–2015, 81 articles) focussing on two different perspectives. First, we provide guidelines on how the information about missingness and related methods should be presented to improve the reporting and handling of missing data. We propose to address this issue by means of a quality evaluation scheme, providing a structured approach that can be used to guide the collection of information, elicitation of the assumptions, choice of methods and considerations of possible limitations of the given missingness problem. Second, we review the description of the missing data, the statistical methods used to deal with them and the quality of the judgement underpinning the choice of these methods. Our review shows that missing data in within-RCT CEAs are still often inadequately handled and the overall level of information provided to support the chosen methods is rarely satisfactory. |
format | Online Article Text |
id | pubmed-5691848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-56918482017-12-18 Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations Gabrio, Andrea Mason, Alexina J. Baio, Gianluca Pharmacoecon Open Review Article Cost-effectiveness analyses (CEAs) alongside randomised controlled trials (RCTs) are increasingly designed to collect resource use and preference-based health status data for the purpose of healthcare technology assessment. However, because of the way these measures are collected, they are prone to missing data, which can ultimately affect the decision of whether an intervention is good value for money. We examine how missing cost and effect outcome data are handled in RCT-based CEAs, complementing a previous review (covering 2003–2009, 88 articles) with a new systematic review (2009–2015, 81 articles) focussing on two different perspectives. First, we provide guidelines on how the information about missingness and related methods should be presented to improve the reporting and handling of missing data. We propose to address this issue by means of a quality evaluation scheme, providing a structured approach that can be used to guide the collection of information, elicitation of the assumptions, choice of methods and considerations of possible limitations of the given missingness problem. Second, we review the description of the missing data, the statistical methods used to deal with them and the quality of the judgement underpinning the choice of these methods. Our review shows that missing data in within-RCT CEAs are still often inadequately handled and the overall level of information provided to support the chosen methods is rarely satisfactory. Springer International Publishing 2017-02-09 /pmc/articles/PMC5691848/ /pubmed/29442336 http://dx.doi.org/10.1007/s41669-017-0015-6 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Review Article Gabrio, Andrea Mason, Alexina J. Baio, Gianluca Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations |
title | Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations |
title_full | Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations |
title_fullStr | Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations |
title_full_unstemmed | Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations |
title_short | Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations |
title_sort | handling missing data in within-trial cost-effectiveness analysis: a review with future recommendations |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5691848/ https://www.ncbi.nlm.nih.gov/pubmed/29442336 http://dx.doi.org/10.1007/s41669-017-0015-6 |
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