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Article: Bayesian Hierarchical Modeling and Biomarker Cutoff Identification in Basket Trials
Title | Bayesian Hierarchical Modeling and Biomarker Cutoff Identification in Basket Trials |
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Authors | |
Keywords | Basket trial Biomarker cutoff Biomarker design Hierarchical model Patient heterogeneity |
Issue Date | 2020 |
Publisher | Taylor & Francis: STM, Behavioural Science and Public Health Titles. The Journal's web site is located at http://tandfonline.com/toc/usbr20/current |
Citation | Statistics in Biopharmaceutical Research, 2020, Epub 2020-09-25 How to Cite? |
Abstract | Patients’ heterogeneity poses a fundamental problem in the rapidly developing field of precision medicine. Based on a prespecified cutoff, biomarker-based designs provide a flexible approach to selecting a subset of biomarker-positive patients who are most likely to benefit from the new therapeutics. However, a natural question is how to determine the biomarker cutoff that distinguishes biomarker-positive patients from the negatives, and then evaluate the efficacy of the new therapeutics in one trial. We propose a Phase II basket biomarker cutoff (BBC) design where a biomarker for identifying the sensitive patients is measured on a continuous scale. The proposed BBC design incorporates the biomarker cutoff identification procedure into a basket trial via Bayesian hierarchical modeling. We verify its feasibility and practicability via real trial examples, extensive simulation studies, and sensitivity analyses. The simulation studies show that the BBC design can select biomarker-positive patients accurately and may exhibit competitive improvement in regards to the overall Type I error, power, and average sample number. |
Persistent Identifier | http://hdl.handle.net/10722/288172 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 0.978 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yin, G | - |
dc.contributor.author | YANG, Z | - |
dc.contributor.author | Odani, M | - |
dc.contributor.author | Fukimbara, S | - |
dc.date.accessioned | 2020-10-05T12:08:56Z | - |
dc.date.available | 2020-10-05T12:08:56Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Statistics in Biopharmaceutical Research, 2020, Epub 2020-09-25 | - |
dc.identifier.issn | 1946-6315 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288172 | - |
dc.description.abstract | Patients’ heterogeneity poses a fundamental problem in the rapidly developing field of precision medicine. Based on a prespecified cutoff, biomarker-based designs provide a flexible approach to selecting a subset of biomarker-positive patients who are most likely to benefit from the new therapeutics. However, a natural question is how to determine the biomarker cutoff that distinguishes biomarker-positive patients from the negatives, and then evaluate the efficacy of the new therapeutics in one trial. We propose a Phase II basket biomarker cutoff (BBC) design where a biomarker for identifying the sensitive patients is measured on a continuous scale. The proposed BBC design incorporates the biomarker cutoff identification procedure into a basket trial via Bayesian hierarchical modeling. We verify its feasibility and practicability via real trial examples, extensive simulation studies, and sensitivity analyses. The simulation studies show that the BBC design can select biomarker-positive patients accurately and may exhibit competitive improvement in regards to the overall Type I error, power, and average sample number. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis: STM, Behavioural Science and Public Health Titles. The Journal's web site is located at http://tandfonline.com/toc/usbr20/current | - |
dc.relation.ispartof | Statistics in Biopharmaceutical Research | - |
dc.rights | This is an electronic version of an article published in [include the complete citation information for the final version of the article as published in the print edition of the journal]. [JOURNAL TITLE] is available online at: http://www.informaworld.com/smpp/ with the open URL of your article. | - |
dc.subject | Basket trial | - |
dc.subject | Biomarker cutoff | - |
dc.subject | Biomarker design | - |
dc.subject | Hierarchical model | - |
dc.subject | Patient heterogeneity | - |
dc.title | Bayesian Hierarchical Modeling and Biomarker Cutoff Identification in Basket Trials | - |
dc.type | Article | - |
dc.identifier.email | Yin, G: gyin@hku.hk | - |
dc.identifier.authority | Yin, G=rp00831 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/19466315.2020.1811146 | - |
dc.identifier.scopus | eid_2-s2.0-85091613714 | - |
dc.identifier.hkuros | 315625 | - |
dc.identifier.volume | Epub 2020-09-25 | - |
dc.identifier.eissn | 1946-6315 | - |
dc.identifier.isi | WOS:000573169800001 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1946-6315 | - |