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Evaluating the effects of design parameters on the performances of phase I trial designs
Numerous designs have been proposed for phase I clinical trials. Although studies have compared their performances, few have considered the effects of changing design parameters. In this article, we review a few popular designs, including the 3 + 3, continuous reassessment method (CRM), Bayesian opt...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543020/ https://www.ncbi.nlm.nih.gov/pubmed/31193764 http://dx.doi.org/10.1016/j.conctc.2019.100379 |
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author | Zhu, Yaqian Hwang, Wei-Ting Li, Yimei |
author_facet | Zhu, Yaqian Hwang, Wei-Ting Li, Yimei |
author_sort | Zhu, Yaqian |
collection | PubMed |
description | Numerous designs have been proposed for phase I clinical trials. Although studies have compared their performances, few have considered the effects of changing design parameters. In this article, we review a few popular designs, including the 3 + 3, continuous reassessment method (CRM), Bayesian optimal interval (BOIN) design, and Keyboard design, and evaluate how varying design parameters (such as number of dose levels, target toxicity rate, maximum sample size, and cohort size) could impact the performances of each design through simulations. Excluded from our analysis is the mTPI-2 design, which operates in the same way as the Keyboard. Our results suggest that regardless of the choices of design parameters, the 3 + 3 design performs worse than the other ones, and BOIN and Keyboard have comparable performance to CRM. For any design, the performance varies with the choice of parameters. In particular, it improves as sample sizes increase, but the magnitude of benefit from increasing sample sizes varies substantially across scenarios. The impact of cohort size on design performances seems to have no clear direction. Therefore, BOIN and Keyboard designs are generally recommended due to their simplicity and good performance. With regard to choices of sample size and cohort size in designing a trial, it is recommend that simulations be performed for the particular clinical settings to aid decision making. |
format | Online Article Text |
id | pubmed-6543020 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-65430202019-06-03 Evaluating the effects of design parameters on the performances of phase I trial designs Zhu, Yaqian Hwang, Wei-Ting Li, Yimei Contemp Clin Trials Commun Article Numerous designs have been proposed for phase I clinical trials. Although studies have compared their performances, few have considered the effects of changing design parameters. In this article, we review a few popular designs, including the 3 + 3, continuous reassessment method (CRM), Bayesian optimal interval (BOIN) design, and Keyboard design, and evaluate how varying design parameters (such as number of dose levels, target toxicity rate, maximum sample size, and cohort size) could impact the performances of each design through simulations. Excluded from our analysis is the mTPI-2 design, which operates in the same way as the Keyboard. Our results suggest that regardless of the choices of design parameters, the 3 + 3 design performs worse than the other ones, and BOIN and Keyboard have comparable performance to CRM. For any design, the performance varies with the choice of parameters. In particular, it improves as sample sizes increase, but the magnitude of benefit from increasing sample sizes varies substantially across scenarios. The impact of cohort size on design performances seems to have no clear direction. Therefore, BOIN and Keyboard designs are generally recommended due to their simplicity and good performance. With regard to choices of sample size and cohort size in designing a trial, it is recommend that simulations be performed for the particular clinical settings to aid decision making. Elsevier 2019-05-17 /pmc/articles/PMC6543020/ /pubmed/31193764 http://dx.doi.org/10.1016/j.conctc.2019.100379 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Zhu, Yaqian Hwang, Wei-Ting Li, Yimei Evaluating the effects of design parameters on the performances of phase I trial designs |
title | Evaluating the effects of design parameters on the performances of phase I trial designs |
title_full | Evaluating the effects of design parameters on the performances of phase I trial designs |
title_fullStr | Evaluating the effects of design parameters on the performances of phase I trial designs |
title_full_unstemmed | Evaluating the effects of design parameters on the performances of phase I trial designs |
title_short | Evaluating the effects of design parameters on the performances of phase I trial designs |
title_sort | evaluating the effects of design parameters on the performances of phase i trial designs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543020/ https://www.ncbi.nlm.nih.gov/pubmed/31193764 http://dx.doi.org/10.1016/j.conctc.2019.100379 |
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