Document Detail

Bayesian statistics in oncology: a guide for the clinical investigator.
MedLine Citation:
PMID:  19691089     Owner:  NLM     Status:  MEDLINE    
The rise of evidence-based medicine as well as important progress in statistical methods and computational power have led to a second birth of the >200-year-old Bayesian framework. The use of Bayesian techniques, in particular in the design and interpretation of clinical trials, offers several substantial advantages over the classical statistical approach. First, in contrast to classical statistics, Bayesian analysis allows a direct statement regarding the probability that a treatment was beneficial. Second, Bayesian statistics allow the researcher to incorporate any prior information in the analysis of the experimental results. Third, Bayesian methods can efficiently handle complex statistical models, which are suited for advanced clinical trial designs. Finally, Bayesian statistics encourage a thorough consideration and presentation of the assumptions underlying an analysis, which enables the reader to fully appraise the authors' conclusions. Both Bayesian and classical statistics have their respective strengths and limitations and should be viewed as being complementary to each other; we do not attempt to make a head-to-head comparison, as this is beyond the scope of the present review. Rather, the objective of the present article is to provide a nonmathematical, reader-friendly overview of the current practice of Bayesian statistics coupled with numerous intuitive examples from the field of oncology. It is hoped that this educational review will be a useful resource to the oncologist and result in a better understanding of the scope, strengths, and limitations of the Bayesian approach.
Michel Adamina; George Tomlinson; Ulrich Guller
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Review    
Journal Detail:
Title:  Cancer     Volume:  115     ISSN:  0008-543X     ISO Abbreviation:  Cancer     Publication Date:  2009 Dec 
Date Detail:
Created Date:  2009-11-25     Completed Date:  2010-01-08     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0374236     Medline TA:  Cancer     Country:  United States    
Other Details:
Languages:  eng     Pagination:  5371-81     Citation Subset:  AIM; IM    
Copyright Information:
(c) 2009 American Cancer Society.
Department of Health Policy, Management and Evaluation, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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MeSH Terms
Bayes Theorem
Clinical Trials as Topic
Medical Oncology / statistics & numerical data*
Random Allocation

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