Extended two-stage adaptive designs with three target responses for phase II clinical trials.
We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon's two-sta...
| Publicado en: | Statistical Methods in Medical Research Vol. 27; no. 12; pp. 3628 - 3643 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Dec2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133160739&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133160739 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Dec2018 vid: 27 iid: 12 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 133160739 133160739 NLM28535716 133160739 10.1177/0962280217709817 NLM28535716 133160739 ppf: 3628 ppct: 15 formats: tig: atl: Extended two-stage adaptive designs with three target responses for phase II clinical trials. aug: au: Kim, Seongho Wong, Weng Kee affil: Biostatistics Core, Karmanos Cancer Institute, USA sug: subj: Study Design Melanoma Drug Therapy Clinical Trials Models, Statistical Sample Size Human Algorithms Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Funding Source ab: We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon's two-stage design and its extension that allows two target response rates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperforms greedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capable of solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of our proposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one of the three target response rates of the drug, subject to various user-specified testing error rates. Our designs are therefore more flexible and interestingly, do not necessarily require larger expected sample size requirements than two-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires one half fewer subjects than the implemented design in the study. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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