Cargando…

Classifying information-sharing methods

BACKGROUND: Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly:...

Descripción completa

Detalles Bibliográficos
Autores principales: Nikolaidis, Georgios F., Woods, Beth, Palmer, Stephen, Soares, Marta O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140466/
https://www.ncbi.nlm.nih.gov/pubmed/34022810
http://dx.doi.org/10.1186/s12874-021-01292-z
_version_ 1783696193947172864
author Nikolaidis, Georgios F.
Woods, Beth
Palmer, Stephen
Soares, Marta O.
author_facet Nikolaidis, Georgios F.
Woods, Beth
Palmer, Stephen
Soares, Marta O.
author_sort Nikolaidis, Georgios F.
collection PubMed
description BACKGROUND: Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences (‘lumping’) or not included at all (‘splitting’). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods. METHODS: Forwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs. RESULTS: Overall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four ’core’ relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches. CONCLUSIONS: This study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four ‘core’ methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12874-021-01292-z).
format Online
Article
Text
id pubmed-8140466
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-81404662021-05-25 Classifying information-sharing methods Nikolaidis, Georgios F. Woods, Beth Palmer, Stephen Soares, Marta O. BMC Med Res Methodol Research Article BACKGROUND: Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences (‘lumping’) or not included at all (‘splitting’). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods. METHODS: Forwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs. RESULTS: Overall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four ’core’ relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches. CONCLUSIONS: This study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four ‘core’ methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s12874-021-01292-z). BioMed Central 2021-05-22 /pmc/articles/PMC8140466/ /pubmed/34022810 http://dx.doi.org/10.1186/s12874-021-01292-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Article
Nikolaidis, Georgios F.
Woods, Beth
Palmer, Stephen
Soares, Marta O.
Classifying information-sharing methods
title Classifying information-sharing methods
title_full Classifying information-sharing methods
title_fullStr Classifying information-sharing methods
title_full_unstemmed Classifying information-sharing methods
title_short Classifying information-sharing methods
title_sort classifying information-sharing methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140466/
https://www.ncbi.nlm.nih.gov/pubmed/34022810
http://dx.doi.org/10.1186/s12874-021-01292-z
work_keys_str_mv AT nikolaidisgeorgiosf classifyinginformationsharingmethods
AT woodsbeth classifyinginformationsharingmethods
AT palmerstephen classifyinginformationsharingmethods
AT soaresmartao classifyinginformationsharingmethods