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Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis

Network meta‐analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper...

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Autores principales: van Valkenhoef, Gert, Dias, Sofia, Ades, A. E., Welton, Nicky J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5057346/
https://www.ncbi.nlm.nih.gov/pubmed/26461181
http://dx.doi.org/10.1002/jrsm.1167
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author van Valkenhoef, Gert
Dias, Sofia
Ades, A. E.
Welton, Nicky J.
author_facet van Valkenhoef, Gert
Dias, Sofia
Ades, A. E.
Welton, Nicky J.
author_sort van Valkenhoef, Gert
collection PubMed
description Network meta‐analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node‐splitting approach, which is particularly attractive because of its straightforward interpretation, contrasting estimates from both direct and indirect evidence. However, node‐splitting analyses are labour‐intensive because each comparison of interest requires a separate model. It would be advantageous if node‐splitting models could be estimated automatically for all comparisons of interest. We present an unambiguous decision rule to choose which comparisons to split, and prove that it selects only comparisons in potentially inconsistent loops in the network, and that all potentially inconsistent loops in the network are investigated. Moreover, the decision rule circumvents problems with the parameterisation of multi‐arm trials, ensuring that model generation is trivial in all cases. Thus, our methods eliminate most of the manual work involved in using the node‐splitting approach, enabling the analyst to focus on interpreting the results. © 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.
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spelling pubmed-50573462016-10-19 Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis van Valkenhoef, Gert Dias, Sofia Ades, A. E. Welton, Nicky J. Res Synth Methods Original Articles Network meta‐analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node‐splitting approach, which is particularly attractive because of its straightforward interpretation, contrasting estimates from both direct and indirect evidence. However, node‐splitting analyses are labour‐intensive because each comparison of interest requires a separate model. It would be advantageous if node‐splitting models could be estimated automatically for all comparisons of interest. We present an unambiguous decision rule to choose which comparisons to split, and prove that it selects only comparisons in potentially inconsistent loops in the network, and that all potentially inconsistent loops in the network are investigated. Moreover, the decision rule circumvents problems with the parameterisation of multi‐arm trials, ensuring that model generation is trivial in all cases. Thus, our methods eliminate most of the manual work involved in using the node‐splitting approach, enabling the analyst to focus on interpreting the results. © 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd. John Wiley and Sons Inc. 2015-10-13 2016-03 /pmc/articles/PMC5057346/ /pubmed/26461181 http://dx.doi.org/10.1002/jrsm.1167 Text en © 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
van Valkenhoef, Gert
Dias, Sofia
Ades, A. E.
Welton, Nicky J.
Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis
title Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis
title_full Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis
title_fullStr Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis
title_full_unstemmed Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis
title_short Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis
title_sort automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5057346/
https://www.ncbi.nlm.nih.gov/pubmed/26461181
http://dx.doi.org/10.1002/jrsm.1167
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