Document Detail


Bayesian versus frequentist hypotheses testing in clinical trials with dichotomous and countable outcomes.
MedLine Citation:
PMID:  20721786     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In the problem of hypothesis testing, a question of practical importance is: When do Bayesian and frequentist methodologies suggest similar solutions? Substantial progress has been made for one-sided hypotheses on the parameters of continuous distributions. In this article, we study the problem of testing one-side hypotheses in binomial and Poisson trials, using Bayesian models with conjugate priors. By correctly choosing prior parameters, we can make the posterior probability smaller than, equal to, or larger than the frequentist p-value. The results are illustrated through simulation modeling and analysis of data from clinical trials.
Authors:
Boris G Zaslavsky
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Journal of biopharmaceutical statistics     Volume:  20     ISSN:  1520-5711     ISO Abbreviation:  J Biopharm Stat     Publication Date:  2010 Sep 
Date Detail:
Created Date:  2010-08-19     Completed Date:  2011-01-14     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9200436     Medline TA:  J Biopharm Stat     Country:  England    
Other Details:
Languages:  eng     Pagination:  985-97     Citation Subset:  IM    
Affiliation:
Food and Drug Administration, Rockville, Maryland 20852, USA. Boris.Zaslavsky@FDA.HHS.gov
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Bayes Theorem
Binomial Distribution
Biostatistics / methods
Blood Transfusion / adverse effects
Clinical Trials as Topic / methods*
Computer Simulation
Factor VIII / therapeutic use
Heart Valve Prosthesis / adverse effects
Hemophilia A / drug therapy
Humans
Models, Statistical*
Mortality
Poisson Distribution
Thromboembolism / etiology
Treatment Outcome
Chemical
Reg. No./Substance:
9001-27-8/Factor VIII

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


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