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A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer

BACKGROUND: Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application...

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Autores principales: Sollfrank, L, Linn, SC, Hauptmann, M, Jóźwiak, K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10308726/
https://www.ncbi.nlm.nih.gov/pubmed/37386356
http://dx.doi.org/10.1186/s12874-023-01982-w
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author Sollfrank, L
Linn, SC
Hauptmann, M
Jóźwiak, K
author_facet Sollfrank, L
Linn, SC
Hauptmann, M
Jóźwiak, K
author_sort Sollfrank, L
collection PubMed
description BACKGROUND: Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used. METHODS: A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported. RESULTS: Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity. CONCLUSIONS: Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01982-w.
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spelling pubmed-103087262023-06-30 A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer Sollfrank, L Linn, SC Hauptmann, M Jóźwiak, K BMC Med Res Methodol Research BACKGROUND: Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used. METHODS: A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported. RESULTS: Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity. CONCLUSIONS: Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01982-w. BioMed Central 2023-06-29 /pmc/articles/PMC10308726/ /pubmed/37386356 http://dx.doi.org/10.1186/s12874-023-01982-w Text en © The Author(s) 2023, corrected publication 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sollfrank, L
Linn, SC
Hauptmann, M
Jóźwiak, K
A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer
title A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer
title_full A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer
title_fullStr A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer
title_full_unstemmed A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer
title_short A scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer
title_sort scoping review of statistical methods in studies of biomarker-related treatment heterogeneity for breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10308726/
https://www.ncbi.nlm.nih.gov/pubmed/37386356
http://dx.doi.org/10.1186/s12874-023-01982-w
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