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Improving clinical trial efficiency by biomarker-guided patient selection
BACKGROUND: In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects fo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229992/ https://www.ncbi.nlm.nih.gov/pubmed/24690215 http://dx.doi.org/10.1186/1745-6215-15-103 |
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author | Boessen, Ruud Heerspink, Hiddo J Lambers De Zeeuw, Dick Grobbee, Diederick E Groenwold, Rolf HH Roes, Kit CB |
author_facet | Boessen, Ruud Heerspink, Hiddo J Lambers De Zeeuw, Dick Grobbee, Diederick E Groenwold, Rolf HH Roes, Kit CB |
author_sort | Boessen, Ruud |
collection | PubMed |
description | BACKGROUND: In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects for inclusion in randomized clinical trials could potentially result in more targeted studies and reduce the number of subjects to recruit. METHODS: This study compared three trial designs on the sample size needed to establish treatment efficacy across a range of realistic scenarios. A conventional parallel group design served as the point of reference, while the alternative designs selected subjects on either a baseline characteristic or an early improvement after a short active run-in phase. Data were generated using a model that characterized the effect of treatment on survival as a combination of a primary effect, an interaction with a baseline marker and/or an early marker improvement. A representative scenario derived from empirical data was also evaluated. RESULTS: Simulations showed that an active run-in design could substantially reduce the number of subjects to recruit when improvement during active run-in was a reliable predictor of differential treatment response. In this case, the baseline selection design was also more efficient than the parallel group design, but less efficient than the active run-in design with an equally restricted population. For most scenarios, however, the advantage of the baseline selection design was limited. CONCLUSIONS: An active run-in design could substantially reduce the number of subjects to recruit in a randomized clinical trial. However, just as with the baseline selection design, generalizability of results may be limited and implementation could be difficult. |
format | Online Article Text |
id | pubmed-4229992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42299922014-11-14 Improving clinical trial efficiency by biomarker-guided patient selection Boessen, Ruud Heerspink, Hiddo J Lambers De Zeeuw, Dick Grobbee, Diederick E Groenwold, Rolf HH Roes, Kit CB Trials Research BACKGROUND: In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects for inclusion in randomized clinical trials could potentially result in more targeted studies and reduce the number of subjects to recruit. METHODS: This study compared three trial designs on the sample size needed to establish treatment efficacy across a range of realistic scenarios. A conventional parallel group design served as the point of reference, while the alternative designs selected subjects on either a baseline characteristic or an early improvement after a short active run-in phase. Data were generated using a model that characterized the effect of treatment on survival as a combination of a primary effect, an interaction with a baseline marker and/or an early marker improvement. A representative scenario derived from empirical data was also evaluated. RESULTS: Simulations showed that an active run-in design could substantially reduce the number of subjects to recruit when improvement during active run-in was a reliable predictor of differential treatment response. In this case, the baseline selection design was also more efficient than the parallel group design, but less efficient than the active run-in design with an equally restricted population. For most scenarios, however, the advantage of the baseline selection design was limited. CONCLUSIONS: An active run-in design could substantially reduce the number of subjects to recruit in a randomized clinical trial. However, just as with the baseline selection design, generalizability of results may be limited and implementation could be difficult. BioMed Central 2014-04-02 /pmc/articles/PMC4229992/ /pubmed/24690215 http://dx.doi.org/10.1186/1745-6215-15-103 Text en Copyright © 2014 Boessen et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 Boessen, Ruud Heerspink, Hiddo J Lambers De Zeeuw, Dick Grobbee, Diederick E Groenwold, Rolf HH Roes, Kit CB Improving clinical trial efficiency by biomarker-guided patient selection |
title | Improving clinical trial efficiency by biomarker-guided patient selection |
title_full | Improving clinical trial efficiency by biomarker-guided patient selection |
title_fullStr | Improving clinical trial efficiency by biomarker-guided patient selection |
title_full_unstemmed | Improving clinical trial efficiency by biomarker-guided patient selection |
title_short | Improving clinical trial efficiency by biomarker-guided patient selection |
title_sort | improving clinical trial efficiency by biomarker-guided patient selection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4229992/ https://www.ncbi.nlm.nih.gov/pubmed/24690215 http://dx.doi.org/10.1186/1745-6215-15-103 |
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