How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand‐Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic
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Le résumé fourni par la source
With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand‐Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic
- Date Crossref
- 05/03/2024
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Tokyo Medical and Dental University Department of Clinical Biostatistics pays non établi dans la noticeUniversité ou école supérieure
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The University of Tokyo Department of Healthcare Quality Assessment pays non établi dans la noticeUniversité ou école supérieure
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National Center for Global Health and Medicine Department of Data Science pays non établi dans la noticeÉtablissement de santé
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National Center For Child Health and Development pays non établi dans la noticeOrganisme public
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St. Marianna University School of Medicine Department of Emergency and Critical Care Medicine pays non établi dans la noticeUniversité ou école supérieure
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Fujita Health University Center for Clinical Trial and Research Support pays non établi dans la noticeUniversité ou école supérieure
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Graduate School of Medical and Dental Sciences Department of Clinical Biostatistics pays non établi dans la noticeUniversité ou école supérieure
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These authors equally contributed to this work pays non établi dans la noticeInstitution
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Center of Clinical Sciences Department of Data Science pays non établi dans la noticeÉtablissement de santé
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Center for Clinical Research pays non établi dans la noticeÉtablissement de santé
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Graduate School of Medicine Department of Healthcare Quality Assessment pays non établi dans la noticeUniversité ou école supérieure
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Center for Clinical Sciences pays non établi dans la noticeÉtablissement de santé
Department of Clinical Biostatistics — Tokyo Medical and Dental University, Department of Healthcare Quality Assessment — The University of Tokyo et Department of Data Science — National Center for Global Health and Medicine, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.