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A Bayesian adaptive design for biomarker trials with linked treatments
BACKGROUND: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. METHODS: We propose a novel Bayesian adaptive rando...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4559835/ https://www.ncbi.nlm.nih.gov/pubmed/26263479 http://dx.doi.org/10.1038/bjc.2015.278 |
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author | Wason, James M S Abraham, Jean E Baird, Richard D Gournaris, Ioannis Vallier, Anne-Laure Brenton, James D Earl, Helena M Mander, Adrian P |
author_facet | Wason, James M S Abraham, Jean E Baird, Richard D Gournaris, Ioannis Vallier, Anne-Laure Brenton, James D Earl, Helena M Mander, Adrian P |
author_sort | Wason, James M S |
collection | PubMed |
description | BACKGROUND: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. METHODS: We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective. RESULTS: Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials. CONCLUSIONS: This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously. |
format | Online Article Text |
id | pubmed-4559835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-45598352015-09-14 A Bayesian adaptive design for biomarker trials with linked treatments Wason, James M S Abraham, Jean E Baird, Richard D Gournaris, Ioannis Vallier, Anne-Laure Brenton, James D Earl, Helena M Mander, Adrian P Br J Cancer Clinical Study BACKGROUND: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. METHODS: We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective. RESULTS: Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials. CONCLUSIONS: This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously. Nature Publishing Group 2015-09-01 2015-08-11 /pmc/articles/PMC4559835/ /pubmed/26263479 http://dx.doi.org/10.1038/bjc.2015.278 Text en Copyright © 2015 Cancer Research UK http://creativecommons.org/licenses/by/4.0/ This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Clinical Study Wason, James M S Abraham, Jean E Baird, Richard D Gournaris, Ioannis Vallier, Anne-Laure Brenton, James D Earl, Helena M Mander, Adrian P A Bayesian adaptive design for biomarker trials with linked treatments |
title | A Bayesian adaptive design for biomarker trials with linked treatments |
title_full | A Bayesian adaptive design for biomarker trials with linked treatments |
title_fullStr | A Bayesian adaptive design for biomarker trials with linked treatments |
title_full_unstemmed | A Bayesian adaptive design for biomarker trials with linked treatments |
title_short | A Bayesian adaptive design for biomarker trials with linked treatments |
title_sort | bayesian adaptive design for biomarker trials with linked treatments |
topic | Clinical Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4559835/ https://www.ncbi.nlm.nih.gov/pubmed/26263479 http://dx.doi.org/10.1038/bjc.2015.278 |
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