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Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values

BACKGROUND: Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta‐analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup o...

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Autores principales: Donegan, Sarah, Welton, Nicky J., Tudur Smith, Catrin, D'Alessandro, Umberto, Dias, Sofia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5724666/
https://www.ncbi.nlm.nih.gov/pubmed/28732142
http://dx.doi.org/10.1002/jrsm.1257
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author Donegan, Sarah
Welton, Nicky J.
Tudur Smith, Catrin
D'Alessandro, Umberto
Dias, Sofia
author_facet Donegan, Sarah
Welton, Nicky J.
Tudur Smith, Catrin
D'Alessandro, Umberto
Dias, Sofia
author_sort Donegan, Sarah
collection PubMed
description BACKGROUND: Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta‐analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup of patients. Two key assumptions underlie such models: consistency of treatment effects and consistency of the regression coefficients for the interactions. Consistency may differ depending on the covariate value at which consistency is assessed. For valid inference, we need to be confident of consistency for the relevant range of covariate values. In this paper, we demonstrate how to assess consistency of treatment effects from direct and indirect evidence at various covariate values. METHODS: Consistency is assessed using visual inspection, inconsistency estimates, and probabilities. The method is applied to an individual patient dataset comparing artemisinin combination therapies for treating uncomplicated malaria in children using the covariate age. RESULTS: The magnitude of the inconsistency appears to be decreasing with increasing age for each comparison. For one comparison, direct and indirect evidence differ for age 1 (P = .05), and this brings results for age 1 for all comparisons into question. CONCLUSION: When fitting models including interactions, the consistency of direct and indirect evidence must be assessed across the range of covariates included in the trials. Clinical inferences are only valid for covariate values for which results are consistent.
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spelling pubmed-57246662017-12-12 Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values Donegan, Sarah Welton, Nicky J. Tudur Smith, Catrin D'Alessandro, Umberto Dias, Sofia Res Synth Methods Original Articles BACKGROUND: Many reviews aim to compare numerous treatments and report results stratified by subgroups (eg, by disease severity). In such cases, a network meta‐analysis model including treatment by covariate interactions can estimate the relative effects of all treatment pairings for each subgroup of patients. Two key assumptions underlie such models: consistency of treatment effects and consistency of the regression coefficients for the interactions. Consistency may differ depending on the covariate value at which consistency is assessed. For valid inference, we need to be confident of consistency for the relevant range of covariate values. In this paper, we demonstrate how to assess consistency of treatment effects from direct and indirect evidence at various covariate values. METHODS: Consistency is assessed using visual inspection, inconsistency estimates, and probabilities. The method is applied to an individual patient dataset comparing artemisinin combination therapies for treating uncomplicated malaria in children using the covariate age. RESULTS: The magnitude of the inconsistency appears to be decreasing with increasing age for each comparison. For one comparison, direct and indirect evidence differ for age 1 (P = .05), and this brings results for age 1 for all comparisons into question. CONCLUSION: When fitting models including interactions, the consistency of direct and indirect evidence must be assessed across the range of covariates included in the trials. Clinical inferences are only valid for covariate values for which results are consistent. John Wiley and Sons Inc. 2017-08-23 2017-12 /pmc/articles/PMC5724666/ /pubmed/28732142 http://dx.doi.org/10.1002/jrsm.1257 Text en © 2017 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
Donegan, Sarah
Welton, Nicky J.
Tudur Smith, Catrin
D'Alessandro, Umberto
Dias, Sofia
Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values
title Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values
title_full Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values
title_fullStr Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values
title_full_unstemmed Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values
title_short Network meta‐analysis including treatment by covariate interactions: Consistency can vary across covariate values
title_sort network meta‐analysis including treatment by covariate interactions: consistency can vary across covariate values
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5724666/
https://www.ncbi.nlm.nih.gov/pubmed/28732142
http://dx.doi.org/10.1002/jrsm.1257
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