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Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment

BACKGROUND: Medical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative...

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Autores principales: Brand, Adam, Sachs, Michael C., Sjölander, Arvid, Gabriel, Erin E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050232/
https://www.ncbi.nlm.nih.gov/pubmed/36690722
http://dx.doi.org/10.1038/s41416-023-02144-x
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author Brand, Adam
Sachs, Michael C.
Sjölander, Arvid
Gabriel, Erin E.
author_facet Brand, Adam
Sachs, Michael C.
Sjölander, Arvid
Gabriel, Erin E.
author_sort Brand, Adam
collection PubMed
description BACKGROUND: Medical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative effectiveness, using predictive biomarkers is becoming more common. RCTs that incorporate predictive biomarkers into the study design, called prediction-driven RCTs, are needed to rigorously evaluate these treatment strategies. Although researched extensively in the experimental treatment setting, literature is lacking in providing guidance about prediction-driven RCTs in the comparative effectiveness setting. METHODS: Realistic simulations with time-to-event endpoints are used to compare contrasts of clinical utility and provide examples of simulated prediction-driven RCTs in the comparative effectiveness setting. RESULTS: Our proposed contrast for clinical utility accurately estimates the true clinical utility in the comparative effectiveness setting while in some scenarios, the contrast used in current literature does not. DISCUSSION: It is important to properly define contrasts of interest according to the treatment setting. Realistic simulations should be used to choose and evaluate the RCT design(s) able to directly estimate that contrast. In the comparative effectiveness setting, our proposed contrast for clinical utility should be used.
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spelling pubmed-100502322023-03-30 Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment Brand, Adam Sachs, Michael C. Sjölander, Arvid Gabriel, Erin E. Br J Cancer Article BACKGROUND: Medical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative effectiveness, using predictive biomarkers is becoming more common. RCTs that incorporate predictive biomarkers into the study design, called prediction-driven RCTs, are needed to rigorously evaluate these treatment strategies. Although researched extensively in the experimental treatment setting, literature is lacking in providing guidance about prediction-driven RCTs in the comparative effectiveness setting. METHODS: Realistic simulations with time-to-event endpoints are used to compare contrasts of clinical utility and provide examples of simulated prediction-driven RCTs in the comparative effectiveness setting. RESULTS: Our proposed contrast for clinical utility accurately estimates the true clinical utility in the comparative effectiveness setting while in some scenarios, the contrast used in current literature does not. DISCUSSION: It is important to properly define contrasts of interest according to the treatment setting. Realistic simulations should be used to choose and evaluate the RCT design(s) able to directly estimate that contrast. In the comparative effectiveness setting, our proposed contrast for clinical utility should be used. Nature Publishing Group UK 2023-01-23 2023-03-30 /pmc/articles/PMC10050232/ /pubmed/36690722 http://dx.doi.org/10.1038/s41416-023-02144-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Brand, Adam
Sachs, Michael C.
Sjölander, Arvid
Gabriel, Erin E.
Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment
title Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment
title_full Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment
title_fullStr Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment
title_full_unstemmed Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment
title_short Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment
title_sort confirmatory prediction-driven rcts in comparative effectiveness settings for cancer treatment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050232/
https://www.ncbi.nlm.nih.gov/pubmed/36690722
http://dx.doi.org/10.1038/s41416-023-02144-x
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