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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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Bayesian models in biostatistics and medicine

Bayesian models in biostatistics and medicine

Chapter:
(p.557) 28 Bayesian models in biostatistics and medicine
Source:
Bayesian Theory and Applications
Author(s):

Peter Müller

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0028

This chapter surveys applications of Bayesian theory in biostatistics. It begins by discussing hierarchical models. Arguably the most tightly regulated and well controlled applications of statistical inference in biomedical research are the design and analysis of clinical trials. While far from being an accepted standard, Bayesian methods can contribute significantly to improving trial designs and to constructing designs for complex experimental layouts. Another example of how the Bayesian paradigm can provide coherent and principled answers to complex inference problems can be found in problems related to the control of multiplicities and massive multiple comparisons. The chapter concludes with a brief review of related research.

Keywords:   Bayesian methods, hierarchical models, biostatistics, biomedical research, clinical trials

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