qTPI: A quasi-toxicity probability interval design for phase I trials with multiple-grade toxicities
The common terminology criteria for adverse events by the National Cancer Institute has greatly facilitated the revolution of drug development and an increasing number of Phase I trials have started to collect multiple-grade toxicity endpoints. Appropriate and yet transparent Phase I statistical des...
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Veröffentlicht in: | Statistical methods in medical research 2023-07, Vol.32 (7), p.1389-1402 |
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creator | Ling, Haodong Shi, Haolun Yuan, Nan Ji, Yuan Lin, Xiaolei |
description | The common terminology criteria for adverse events by the National Cancer Institute has greatly facilitated the revolution of drug development and an increasing number of Phase I trials have started to collect multiple-grade toxicity endpoints. Appropriate and yet transparent Phase I statistical designs for multiple-grade toxicities are therefore in great needs. In this article, we propose a quasi-toxicity probability interval (qTPI) design that incorporates a quasi-continuous measure of the toxicity probability (
q
T
P
) into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to
q
T
P
according to a severity weight matrix. Dose–toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. Finally, a hypothetical soft tissue sarcoma trial with six toxicity types and grade 0 to grade 4 severity grades is illustrated with patient-by-patient dose allocation under the qTPI design. |
doi_str_mv | 10.1177/09622802231176034 |
format | Article |
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q
T
P
) into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to
q
T
P
according to a severity weight matrix. Dose–toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. Finally, a hypothetical soft tissue sarcoma trial with six toxicity types and grade 0 to grade 4 severity grades is illustrated with patient-by-patient dose allocation under the qTPI design.</description><identifier>ISSN: 0962-2802</identifier><identifier>EISSN: 1477-0334</identifier><identifier>DOI: 10.1177/09622802231176034</identifier><identifier>PMID: 37278183</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Bayesian analysis ; Cancer ; Critical incidents ; Dosage ; Drug development ; Elicitation ; Probability ; Reliability ; Sarcoma ; Soft tissues ; Specification ; Statistical analysis ; Terminology ; Toxicity</subject><ispartof>Statistical methods in medical research, 2023-07, Vol.32 (7), p.1389-1402</ispartof><rights>The Author(s) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c320t-fc383a9096de3ef2600e92be5b86252490bbf6fd99e04eba4240fffcfc0a89a13</cites><orcidid>0000-0003-4712-5567 ; 0000-0003-2463-1272 ; 0000-0001-6983-8416</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/09622802231176034$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/09622802231176034$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,776,780,21798,27901,27902,30976,43597,43598</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37278183$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ling, Haodong</creatorcontrib><creatorcontrib>Shi, Haolun</creatorcontrib><creatorcontrib>Yuan, Nan</creatorcontrib><creatorcontrib>Ji, Yuan</creatorcontrib><creatorcontrib>Lin, Xiaolei</creatorcontrib><title>qTPI: A quasi-toxicity probability interval design for phase I trials with multiple-grade toxicities</title><title>Statistical methods in medical research</title><addtitle>Stat Methods Med Res</addtitle><description>The common terminology criteria for adverse events by the National Cancer Institute has greatly facilitated the revolution of drug development and an increasing number of Phase I trials have started to collect multiple-grade toxicity endpoints. Appropriate and yet transparent Phase I statistical designs for multiple-grade toxicities are therefore in great needs. In this article, we propose a quasi-toxicity probability interval (qTPI) design that incorporates a quasi-continuous measure of the toxicity probability (
q
T
P
) into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to
q
T
P
according to a severity weight matrix. Dose–toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. 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Appropriate and yet transparent Phase I statistical designs for multiple-grade toxicities are therefore in great needs. In this article, we propose a quasi-toxicity probability interval (qTPI) design that incorporates a quasi-continuous measure of the toxicity probability (
q
T
P
) into the Bayesian theoretic framework of the interval based designs. Multiple-grade toxicity outcomes of each patient are mapped to
q
T
P
according to a severity weight matrix. Dose–toxicity curve underlying the dosing decisions in the qTPI design is continuously updated using accumulating trial data. Numerical simulations investigating the operating characteristics of qTPI show that qTPI achieved better safety, accuracy and reliability compared to designs that rely on binary toxicity data. Furthermore, parameter elicitation in qTPI is simple and does not involve multiple hypothetical cohorts specification. Finally, a hypothetical soft tissue sarcoma trial with six toxicity types and grade 0 to grade 4 severity grades is illustrated with patient-by-patient dose allocation under the qTPI design.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>37278183</pmid><doi>10.1177/09622802231176034</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4712-5567</orcidid><orcidid>https://orcid.org/0000-0003-2463-1272</orcidid><orcidid>https://orcid.org/0000-0001-6983-8416</orcidid></addata></record> |
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source | Applied Social Sciences Index & Abstracts (ASSIA); SAGE Complete |
subjects | Bayesian analysis Cancer Critical incidents Dosage Drug development Elicitation Probability Reliability Sarcoma Soft tissues Specification Statistical analysis Terminology Toxicity |
title | qTPI: A quasi-toxicity probability interval design for phase I trials with multiple-grade toxicities |
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