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Adaptive designs for subpopulation analysis optimizing utility functions
If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is e...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4314682/ https://www.ncbi.nlm.nih.gov/pubmed/25399844 http://dx.doi.org/10.1002/bimj.201300257 |
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author | Graf, Alexandra C Posch, Martin Koenig, Franz |
author_facet | Graf, Alexandra C Posch, Martin Koenig, Franz |
author_sort | Graf, Alexandra C |
collection | PubMed |
description | If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios. |
format | Online Article Text |
id | pubmed-4314682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-43146822015-02-04 Adaptive designs for subpopulation analysis optimizing utility functions Graf, Alexandra C Posch, Martin Koenig, Franz Biom J Decision-Analytic Frameworks If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios. Blackwell Publishing Ltd 2015-01 2014-11-14 /pmc/articles/PMC4314682/ /pubmed/25399844 http://dx.doi.org/10.1002/bimj.201300257 Text en © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim http://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Decision-Analytic Frameworks Graf, Alexandra C Posch, Martin Koenig, Franz Adaptive designs for subpopulation analysis optimizing utility functions |
title | Adaptive designs for subpopulation analysis optimizing utility functions |
title_full | Adaptive designs for subpopulation analysis optimizing utility functions |
title_fullStr | Adaptive designs for subpopulation analysis optimizing utility functions |
title_full_unstemmed | Adaptive designs for subpopulation analysis optimizing utility functions |
title_short | Adaptive designs for subpopulation analysis optimizing utility functions |
title_sort | adaptive designs for subpopulation analysis optimizing utility functions |
topic | Decision-Analytic Frameworks |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4314682/ https://www.ncbi.nlm.nih.gov/pubmed/25399844 http://dx.doi.org/10.1002/bimj.201300257 |
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