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Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine

BACKGROUND: Although subgroup analyses in clinical trials may provide evidence for individualised medicine, their conduct and interpretation remain controversial. METHODS: Subgroup effect can be defined as the difference in treatment effect across patient subgroups. Cumulative subgroup analysis refe...

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Autores principales: Song, Fujian, Bachmann, Max O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5157082/
https://www.ncbi.nlm.nih.gov/pubmed/27974045
http://dx.doi.org/10.1186/s12916-016-0744-x
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author Song, Fujian
Bachmann, Max O.
author_facet Song, Fujian
Bachmann, Max O.
author_sort Song, Fujian
collection PubMed
description BACKGROUND: Although subgroup analyses in clinical trials may provide evidence for individualised medicine, their conduct and interpretation remain controversial. METHODS: Subgroup effect can be defined as the difference in treatment effect across patient subgroups. Cumulative subgroup analysis refers to a series of repeated pooling of subgroup effects after adding data from each of related trials chronologically, to investigate the accumulating evidence for subgroup effects. We illustrated the clinical relevance of cumulative subgroup analysis in two case studies using data from published individual patient data (IPD) meta-analyses. Computer simulations were also conducted to examine the statistical properties of cumulative subgroup analysis. RESULTS: In case study 1, an IPD meta-analysis of 10 randomised trials (RCTs) on beta blockers for heart failure reported significant interaction of treatment effects with baseline rhythm. Cumulative subgroup analysis could have detected the subgroup effect 15 years earlier, with five fewer trials and 71% less patients, than the IPD meta-analysis which first reported it. Case study 2 involved an IPD meta-analysis of 11 RCTs on treatments for pulmonary arterial hypertension that reported significant subgroup effect by aetiology. Cumulative subgroup analysis could have detected the subgroup effect 6 years earlier, with three fewer trials and 40% less patients than the IPD meta-analysis. Computer simulations have indicated that cumulative subgroup analysis increases the statistical power and is not associated with inflated false positives. CONCLUSIONS: To reduce waste of research data, subgroup analyses in clinical trials should be more widely conducted and adequately reported so that cumulative subgroup analyses could be timely performed to inform clinical practice and further research. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-016-0744-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-51570822016-12-20 Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine Song, Fujian Bachmann, Max O. BMC Med Research Article BACKGROUND: Although subgroup analyses in clinical trials may provide evidence for individualised medicine, their conduct and interpretation remain controversial. METHODS: Subgroup effect can be defined as the difference in treatment effect across patient subgroups. Cumulative subgroup analysis refers to a series of repeated pooling of subgroup effects after adding data from each of related trials chronologically, to investigate the accumulating evidence for subgroup effects. We illustrated the clinical relevance of cumulative subgroup analysis in two case studies using data from published individual patient data (IPD) meta-analyses. Computer simulations were also conducted to examine the statistical properties of cumulative subgroup analysis. RESULTS: In case study 1, an IPD meta-analysis of 10 randomised trials (RCTs) on beta blockers for heart failure reported significant interaction of treatment effects with baseline rhythm. Cumulative subgroup analysis could have detected the subgroup effect 15 years earlier, with five fewer trials and 71% less patients, than the IPD meta-analysis which first reported it. Case study 2 involved an IPD meta-analysis of 11 RCTs on treatments for pulmonary arterial hypertension that reported significant subgroup effect by aetiology. Cumulative subgroup analysis could have detected the subgroup effect 6 years earlier, with three fewer trials and 40% less patients than the IPD meta-analysis. Computer simulations have indicated that cumulative subgroup analysis increases the statistical power and is not associated with inflated false positives. CONCLUSIONS: To reduce waste of research data, subgroup analyses in clinical trials should be more widely conducted and adequately reported so that cumulative subgroup analyses could be timely performed to inform clinical practice and further research. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-016-0744-x) contains supplementary material, which is available to authorized users. BioMed Central 2016-12-15 /pmc/articles/PMC5157082/ /pubmed/27974045 http://dx.doi.org/10.1186/s12916-016-0744-x Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Song, Fujian
Bachmann, Max O.
Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine
title Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine
title_full Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine
title_fullStr Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine
title_full_unstemmed Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine
title_short Cumulative subgroup analysis to reduce waste in clinical research for individualised medicine
title_sort cumulative subgroup analysis to reduce waste in clinical research for individualised medicine
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5157082/
https://www.ncbi.nlm.nih.gov/pubmed/27974045
http://dx.doi.org/10.1186/s12916-016-0744-x
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