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TOP: Time-to-Event Bayesian Optimal Phase II Trial Design for Cancer Immunotherapy
Abstract Background Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more appropriate in some trials. These new features o...
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Published in: | JNCI : Journal of the National Cancer Institute 2020-01, Vol.112 (1), p.38-45 |
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creator | Lin, Ruitao Coleman, Robert L Yuan, Ying |
description | Abstract
Background
Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more appropriate in some trials. These new features of immunotherapy make conventional phase II trial designs, which assume a single binary endpoint that is quickly ascertainable, inefficient and dysfunctional.
Methods
We propose a flexible and efficient time-to-event Bayesian optimal phase II (TOP) design. The TOP design is efficient in that it allows real-time “go/no-go” interim decision making in the presence of late-onset responses by using all available data and maximizes statistical power for detecting effective treatments. TOP is flexible in the number of interim looks and capable of handling simple and complicated endpoints under a unified framework. We conduct simulation studies to evaluate the operating characteristics of the TOP design.
Results
In the considered trial settings, compared to some existing Bayesian designs, the TOP design shortens the trial duration by 4–10 months and improves the power to detect effective treatment up to 90%, with well-controlled type I errors.
Conclusions
The TOP design is transparent and easy to implement, as its decision rules can be tabulated and included in the protocol prior to the conduct of the trial. The TOP design provides a flexible, efficient, and easy-to-implement method to accelerate and improve the development of immunotherapies. |
doi_str_mv | 10.1093/jnci/djz049 |
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Background
Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more appropriate in some trials. These new features of immunotherapy make conventional phase II trial designs, which assume a single binary endpoint that is quickly ascertainable, inefficient and dysfunctional.
Methods
We propose a flexible and efficient time-to-event Bayesian optimal phase II (TOP) design. The TOP design is efficient in that it allows real-time “go/no-go” interim decision making in the presence of late-onset responses by using all available data and maximizes statistical power for detecting effective treatments. TOP is flexible in the number of interim looks and capable of handling simple and complicated endpoints under a unified framework. We conduct simulation studies to evaluate the operating characteristics of the TOP design.
Results
In the considered trial settings, compared to some existing Bayesian designs, the TOP design shortens the trial duration by 4–10 months and improves the power to detect effective treatment up to 90%, with well-controlled type I errors.
Conclusions
The TOP design is transparent and easy to implement, as its decision rules can be tabulated and included in the protocol prior to the conduct of the trial. The TOP design provides a flexible, efficient, and easy-to-implement method to accelerate and improve the development of immunotherapies.</description><identifier>ISSN: 0027-8874</identifier><identifier>EISSN: 1460-2105</identifier><identifier>DOI: 10.1093/jnci/djz049</identifier><identifier>PMID: 30924863</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Bayesian analysis ; Cancer ; Cancer immunotherapy ; Decision making ; Design ; Immunotherapy</subject><ispartof>JNCI : Journal of the National Cancer Institute, 2020-01, Vol.112 (1), p.38-45</ispartof><rights>The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com. 2019</rights><rights>The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-35641e3380cf95c700704e95447a60d8643cae031491e27b7b08b8e3d2ff06343</citedby><cites>FETCH-LOGICAL-c440t-35641e3380cf95c700704e95447a60d8643cae031491e27b7b08b8e3d2ff06343</cites><orcidid>0000-0003-2244-131X ; 0000-0003-3163-480X ; 0000-0001-9343-8754</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30924863$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Ruitao</creatorcontrib><creatorcontrib>Coleman, Robert L</creatorcontrib><creatorcontrib>Yuan, Ying</creatorcontrib><title>TOP: Time-to-Event Bayesian Optimal Phase II Trial Design for Cancer Immunotherapy</title><title>JNCI : Journal of the National Cancer Institute</title><addtitle>J Natl Cancer Inst</addtitle><description>Abstract
Background
Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more appropriate in some trials. These new features of immunotherapy make conventional phase II trial designs, which assume a single binary endpoint that is quickly ascertainable, inefficient and dysfunctional.
Methods
We propose a flexible and efficient time-to-event Bayesian optimal phase II (TOP) design. The TOP design is efficient in that it allows real-time “go/no-go” interim decision making in the presence of late-onset responses by using all available data and maximizes statistical power for detecting effective treatments. TOP is flexible in the number of interim looks and capable of handling simple and complicated endpoints under a unified framework. We conduct simulation studies to evaluate the operating characteristics of the TOP design.
Results
In the considered trial settings, compared to some existing Bayesian designs, the TOP design shortens the trial duration by 4–10 months and improves the power to detect effective treatment up to 90%, with well-controlled type I errors.
Conclusions
The TOP design is transparent and easy to implement, as its decision rules can be tabulated and included in the protocol prior to the conduct of the trial. The TOP design provides a flexible, efficient, and easy-to-implement method to accelerate and improve the development of immunotherapies.</description><subject>Bayesian analysis</subject><subject>Cancer</subject><subject>Cancer immunotherapy</subject><subject>Decision making</subject><subject>Design</subject><subject>Immunotherapy</subject><issn>0027-8874</issn><issn>1460-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kV1r2zAUhsVYadOsV7sfgkEpFLdHH7HsXRS29CtQSBnZtVCU48bBljzJLmS_fgppy7aL6eYgzsPLI72EfGRwwaAUlxtn68vV5hfI8h0ZMZlDxhlM3pMRAFdZUSh5RI5j3EA6JZeH5EjsZpGLEfm-mD9-oYu6xaz32c0zup5-M1uMtXF03vV1axr6uDYR6WxGF6FO1-u0fXK08oFOjbMY6KxtB-f7NQbTbT-Qg8o0EU9e5pj8uL1ZTO-zh_ndbPr1IbNSQp-JSS4ZClGArcqJVQAKJJYTKZXJYVXkUliDIJgsGXK1VEsolgWKFa8qyIUUY3K1z-2GZYsrm9SDaXQXknPYam9q_ffG1Wv95J91wUGq9HNjcvYSEPzPAWOv2zpabBrj0A9Rc56clATGE_r5H3Tjh-DS8zSXApgSooREne8pG3yMAas3GQZ615XedaX3XSX605_-b-xrOQk43QN-6P6b9Bs9k5vJ</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Lin, Ruitao</creator><creator>Coleman, Robert L</creator><creator>Yuan, Ying</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TO</scope><scope>7U7</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2244-131X</orcidid><orcidid>https://orcid.org/0000-0003-3163-480X</orcidid><orcidid>https://orcid.org/0000-0001-9343-8754</orcidid></search><sort><creationdate>20200101</creationdate><title>TOP: Time-to-Event Bayesian Optimal Phase II Trial Design for Cancer Immunotherapy</title><author>Lin, Ruitao ; Coleman, Robert L ; Yuan, Ying</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-35641e3380cf95c700704e95447a60d8643cae031491e27b7b08b8e3d2ff06343</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Bayesian analysis</topic><topic>Cancer</topic><topic>Cancer immunotherapy</topic><topic>Decision making</topic><topic>Design</topic><topic>Immunotherapy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Ruitao</creatorcontrib><creatorcontrib>Coleman, Robert L</creatorcontrib><creatorcontrib>Yuan, Ying</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>JNCI : Journal of the National Cancer Institute</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Ruitao</au><au>Coleman, Robert L</au><au>Yuan, Ying</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>TOP: Time-to-Event Bayesian Optimal Phase II Trial Design for Cancer Immunotherapy</atitle><jtitle>JNCI : Journal of the National Cancer Institute</jtitle><addtitle>J Natl Cancer Inst</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>112</volume><issue>1</issue><spage>38</spage><epage>45</epage><pages>38-45</pages><issn>0027-8874</issn><eissn>1460-2105</eissn><abstract>Abstract
Background
Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more appropriate in some trials. These new features of immunotherapy make conventional phase II trial designs, which assume a single binary endpoint that is quickly ascertainable, inefficient and dysfunctional.
Methods
We propose a flexible and efficient time-to-event Bayesian optimal phase II (TOP) design. The TOP design is efficient in that it allows real-time “go/no-go” interim decision making in the presence of late-onset responses by using all available data and maximizes statistical power for detecting effective treatments. TOP is flexible in the number of interim looks and capable of handling simple and complicated endpoints under a unified framework. We conduct simulation studies to evaluate the operating characteristics of the TOP design.
Results
In the considered trial settings, compared to some existing Bayesian designs, the TOP design shortens the trial duration by 4–10 months and improves the power to detect effective treatment up to 90%, with well-controlled type I errors.
Conclusions
The TOP design is transparent and easy to implement, as its decision rules can be tabulated and included in the protocol prior to the conduct of the trial. The TOP design provides a flexible, efficient, and easy-to-implement method to accelerate and improve the development of immunotherapies.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>30924863</pmid><doi>10.1093/jnci/djz049</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-2244-131X</orcidid><orcidid>https://orcid.org/0000-0003-3163-480X</orcidid><orcidid>https://orcid.org/0000-0001-9343-8754</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Bayesian analysis Cancer Cancer immunotherapy Decision making Design Immunotherapy |
title | TOP: Time-to-Event Bayesian Optimal Phase II Trial Design for Cancer Immunotherapy |
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