Decision-theoretic designs for dose-finding clinical trials with multiple outcomes
A decision‐theoretic framework is proposed for designing sequential dose‐finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose‐finding studies involving k doses, the computational complexity is the same as the bandit problem w...
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| Veröffentlicht in: | Statistics in medicine 2006-05, Vol.25 (10), p.1699-1714 |
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| creator | Fan, Shenghua K. Wang, You-Gan |
| description | A decision‐theoretic framework is proposed for designing sequential dose‐finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose‐finding studies involving k doses, the computational complexity is the same as the bandit problem with k‐dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available. Copyright © 2005 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.2322 |
| format | Article |
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The optimal strategy is solvable theoretically via backward induction. However, for dose‐finding studies involving k doses, the computational complexity is the same as the bandit problem with k‐dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available. Copyright © 2005 John Wiley & Sons, Ltd.</description><identifier>ISSN: 0277-6715</identifier><identifier>EISSN: 1097-0258</identifier><identifier>DOI: 10.1002/sim.2322</identifier><identifier>PMID: 16217860</identifier><identifier>CODEN: SMEDDA</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Algorithms ; Anti-HIV Agents - pharmacology ; Antineoplastic Agents, Alkylating - administration & dosage ; Antineoplastic Agents, Alkylating - adverse effects ; bandit process ; Child, Preschool ; Clinical outcomes ; Clinical trials ; Clinical Trials, Phase I as Topic - methods ; Computer Simulation ; Decision Theory ; Drug-Related Side Effects and Adverse Reactions ; HIV Infections - drug therapy ; Humans ; Ifosfamide - administration & dosage ; Ifosfamide - adverse effects ; Medicine ; optimality ; Pharmaceutical Preparations - administration & dosage ; Sarcoma - drug therapy ; sequential clinical trials ; Statistics ; sub-optimality</subject><ispartof>Statistics in medicine, 2006-05, Vol.25 (10), p.1699-1714</ispartof><rights>Copyright © 2005 John Wiley & Sons, Ltd.</rights><rights>Copyright 2006 John Wiley & Sons, Ltd.</rights><rights>Copyright John Wiley and Sons, Limited May 30, 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3842-821577137495fe0dfa4cba01716c489638fe9d844d0063269f3a46d189149ac23</citedby><cites>FETCH-LOGICAL-c3842-821577137495fe0dfa4cba01716c489638fe9d844d0063269f3a46d189149ac23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fsim.2322$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fsim.2322$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16217860$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fan, Shenghua K.</creatorcontrib><creatorcontrib>Wang, You-Gan</creatorcontrib><title>Decision-theoretic designs for dose-finding clinical trials with multiple outcomes</title><title>Statistics in medicine</title><addtitle>Statist. Med</addtitle><description>A decision‐theoretic framework is proposed for designing sequential dose‐finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose‐finding studies involving k doses, the computational complexity is the same as the bandit problem with k‐dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available. Copyright © 2005 John Wiley & Sons, Ltd.</description><subject>Algorithms</subject><subject>Anti-HIV Agents - pharmacology</subject><subject>Antineoplastic Agents, Alkylating - administration & dosage</subject><subject>Antineoplastic Agents, Alkylating - adverse effects</subject><subject>bandit process</subject><subject>Child, Preschool</subject><subject>Clinical outcomes</subject><subject>Clinical trials</subject><subject>Clinical Trials, Phase I as Topic - methods</subject><subject>Computer Simulation</subject><subject>Decision Theory</subject><subject>Drug-Related Side Effects and Adverse Reactions</subject><subject>HIV Infections - drug therapy</subject><subject>Humans</subject><subject>Ifosfamide - administration & dosage</subject><subject>Ifosfamide - adverse effects</subject><subject>Medicine</subject><subject>optimality</subject><subject>Pharmaceutical Preparations - administration & dosage</subject><subject>Sarcoma - drug therapy</subject><subject>sequential clinical trials</subject><subject>Statistics</subject><subject>sub-optimality</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10E9LwzAcxvEgiptT8BVI8OSlM_-atEedOgdTmVMEL6FLUo22zUxa5t69HSt68pRDPjw_-AJwjNEQI0TOgy2HhBKyA_oYpSJCJE52QR8RISIucNwDByF8IIRxTMQ-6GFOsEg46oPHK6NssK6K6nfjvKmtgtoE-1YFmDsPtQsmym2lbfUGVWErq7IC1t5mRYArW7_DsilquywMdE2tXGnCIdjL219z1L0D8Hxz_TS6jaYP48noYhopmjASJQTHQmAqWBrnBuk8Y2qRISwwVyxJOU1yk-qEMY0Qp4SnOc0Y1zhJMUszRegAnG53l959NSbU8sM1vmpPSkIoZm2EuEVnW6S8C8GbXC69LTO_lhjJTTvZtpObdi096faaRWn0H-xitSDagpUtzPrfITmf3HWDnbehNt-_PvOfkgsqYvlyP5Z8FvPZHF3KV_oDerWGNg</recordid><startdate>20060530</startdate><enddate>20060530</enddate><creator>Fan, Shenghua K.</creator><creator>Wang, You-Gan</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope></search><sort><creationdate>20060530</creationdate><title>Decision-theoretic designs for dose-finding clinical trials with multiple outcomes</title><author>Fan, Shenghua K. ; Wang, You-Gan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3842-821577137495fe0dfa4cba01716c489638fe9d844d0063269f3a46d189149ac23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Anti-HIV Agents - pharmacology</topic><topic>Antineoplastic Agents, Alkylating - administration & dosage</topic><topic>Antineoplastic Agents, Alkylating - adverse effects</topic><topic>bandit process</topic><topic>Child, Preschool</topic><topic>Clinical outcomes</topic><topic>Clinical trials</topic><topic>Clinical Trials, Phase I as Topic - methods</topic><topic>Computer Simulation</topic><topic>Decision Theory</topic><topic>Drug-Related Side Effects and Adverse Reactions</topic><topic>HIV Infections - drug therapy</topic><topic>Humans</topic><topic>Ifosfamide - administration & dosage</topic><topic>Ifosfamide - adverse effects</topic><topic>Medicine</topic><topic>optimality</topic><topic>Pharmaceutical Preparations - administration & dosage</topic><topic>Sarcoma - drug therapy</topic><topic>sequential clinical trials</topic><topic>Statistics</topic><topic>sub-optimality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Shenghua K.</creatorcontrib><creatorcontrib>Wang, You-Gan</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Statistics in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fan, Shenghua K.</au><au>Wang, You-Gan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Decision-theoretic designs for dose-finding clinical trials with multiple outcomes</atitle><jtitle>Statistics in medicine</jtitle><addtitle>Statist. Med</addtitle><date>2006-05-30</date><risdate>2006</risdate><volume>25</volume><issue>10</issue><spage>1699</spage><epage>1714</epage><pages>1699-1714</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><coden>SMEDDA</coden><abstract>A decision‐theoretic framework is proposed for designing sequential dose‐finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose‐finding studies involving k doses, the computational complexity is the same as the bandit problem with k‐dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available. Copyright © 2005 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>16217860</pmid><doi>10.1002/sim.2322</doi><tpages>16</tpages></addata></record> |
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| subjects | Algorithms Anti-HIV Agents - pharmacology Antineoplastic Agents, Alkylating - administration & dosage Antineoplastic Agents, Alkylating - adverse effects bandit process Child, Preschool Clinical outcomes Clinical trials Clinical Trials, Phase I as Topic - methods Computer Simulation Decision Theory Drug-Related Side Effects and Adverse Reactions HIV Infections - drug therapy Humans Ifosfamide - administration & dosage Ifosfamide - adverse effects Medicine optimality Pharmaceutical Preparations - administration & dosage Sarcoma - drug therapy sequential clinical trials Statistics sub-optimality |
| title | Decision-theoretic designs for dose-finding clinical trials with multiple outcomes |
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