Document Detail


Bayesian design and conduct of phase II single-arm clinical trials with binary outcomes: a tutorial.
MedLine Citation:
PMID:  18201945     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The aim of phase II single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase trials as they take into account information that accrues during a trial. Predictive probabilities are then updated and so become more accurate as the trial progresses. Suitable priors can act as pseudo samples, which make small sample clinical trials more informative. Thus patients have better chances to receive better treatments. The goal of this paper is to provide a tutorial for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, real data from three clinical trials are presented as examples to illustrate how to conduct a Bayesian approach for phase II single-arm clinical trials with binary outcomes.
Authors:
Sarah Zohar; Satoshi Teramukai; Yinghui Zhou
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Publication Detail:
Type:  Journal Article     Date:  2007-12-04
Journal Detail:
Title:  Contemporary clinical trials     Volume:  29     ISSN:  1559-2030     ISO Abbreviation:  -     Publication Date:  2008 Jul 
Date Detail:
Created Date:  2008-05-30     Completed Date:  2008-09-15     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101242342     Medline TA:  Contemp Clin Trials     Country:  United States    
Other Details:
Languages:  eng     Pagination:  608-16     Citation Subset:  IM    
Affiliation:
Inserm, CIC 9504, Centre d'Investigations Cliniques, Hôpital Saint Louis, Paris, France. sarah.zohar@univ-paris-diderot.fr
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem*
Clinical Trials, Phase II as Topic / methods*
Humans
Models, Statistical
Sample Size

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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