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Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials
Inconsistency can be thought of as a conflict between “direct” evidence on a comparison between treatments B and C and “indirect” evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3704208/ https://www.ncbi.nlm.nih.gov/pubmed/23804508 http://dx.doi.org/10.1177/0272989X12455847 |
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author | Dias, Sofia Welton, Nicky J. Sutton, Alex J. Caldwell, Deborah M. Lu, Guobing Ades, A. E. |
author_facet | Dias, Sofia Welton, Nicky J. Sutton, Alex J. Caldwell, Deborah M. Lu, Guobing Ades, A. E. |
author_sort | Dias, Sofia |
collection | PubMed |
description | Inconsistency can be thought of as a conflict between “direct” evidence on a comparison between treatments B and C and “indirect” evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed. |
format | Online Article Text |
id | pubmed-3704208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-37042082013-07-09 Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials Dias, Sofia Welton, Nicky J. Sutton, Alex J. Caldwell, Deborah M. Lu, Guobing Ades, A. E. Med Decis Making Articles Inconsistency can be thought of as a conflict between “direct” evidence on a comparison between treatments B and C and “indirect” evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed. SAGE Publications 2013-07 /pmc/articles/PMC3704208/ /pubmed/23804508 http://dx.doi.org/10.1177/0272989X12455847 Text en http://creativecommons.org/licenses/by-nc/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Dias, Sofia Welton, Nicky J. Sutton, Alex J. Caldwell, Deborah M. Lu, Guobing Ades, A. E. Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials |
title | Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials |
title_full | Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials |
title_fullStr | Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials |
title_full_unstemmed | Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials |
title_short | Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials |
title_sort | evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3704208/ https://www.ncbi.nlm.nih.gov/pubmed/23804508 http://dx.doi.org/10.1177/0272989X12455847 |
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