title: An extension of Bayesian predictive sample size selection designs for monitoring efficacy and safety.
abstract: Most exploratory clinical trials in cancer are designed as single-arm trials using a binary efficacy outcome with or without interim monitoring. In this context, we have proposed a Bayesian adaptive design denoted as predictive sample size selection design (PSSD), which considered a binary efficacy outcome associated with early futility stopping (Statistics in Medicine 2012; 31: 4243-4254). As a matter of course, it would be more ethical and informative to evaluate safety as well as efficacy during the course of a trial. However, in most of the trials, only major adverse events are taken into account for early termination of the trial, and safety itself is used as a secondary endpoint. In this paper, we propose an extension of the PSSD to monitor efficacy, take into consideration the sample size adaptation during the trial and add continuous monitoring of safety to the trial design. This method is developed in the Bayesian framework, in which a decision to stop for reasons of safety can be made based on the posterior probability or predictive probability, not necessarily at the time of pre-specified monitoring for efficacy. We investigate the operating characteristics of the proposed method through simulation studies and show that the posterior probability-based method with less informative prior to monitor safety has more reasonable performance.
authors.affiliation: Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
authors.forename: Satoshi
authors.identifier: http://orcid.org/0000-0003-2184-0597
authors.initials: S
authors.lastname: Teramukai
authors.affiliation: Department of Biostatistics, Hyogo College of Medicine, Hyogo, Japan.
authors.forename: Takashi
authors.identifier: http://orcid.org/0000-0001-8930-8762
authors.initials: T
authors.lastname: Daimon
authors.affiliation: INSERM, U1138 team 22, Centre de Recherche des Cordeliers, University Paris 5, University Paris 6, Paris, France.
authors.forename: Sarah
authors.initials: S
authors.lastname: Zohar
mesh_terms: D000970:Antineoplastic Agents / Q000008:administration & dosage; D001499:Bayes Theorem; D002648:Child; D017322:Clinical Trials, Phase II as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D016903:Drug Monitoring / Q000379:methods / Q000706:statistics & numerical data*; D000069585:Filgrastim; D016179:Granulocyte Colony-Stimulating Factor / Q000008:administration & dosage; D006412:Hematopoietic Stem Cells / Q000187:drug effects; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy; D011092:Polyethylene Glycols; D011994:Recombinant Proteins / Q000008:administration & dosage; D012107:Research Design / Q000706:statistics & numerical data*; D018401:Sample Size
chemical_list: D000970:Antineoplastic Agents; D011994:Recombinant Proteins; D016179:Granulocyte Colony-Stimulating Factor; C455861:pegfilgrastim; D011092:Polyethylene Glycols; D000069585:Filgrastim
keywords: Bayesian approach; adaptive design; interim monitoring; sample size re-estimation; single-arm oncology trials