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Guidance on the implementation and reporting of a drug safety Bayesian network meta‐analysis
Author(s) -
Ohlssen David,
Price Karen L.,
Amy Xia H.,
Hong Hwanhee,
Kerman Jouni,
Fu Haoda,
Quartey George,
Heilmann Cory R.,
Ma Haijun,
Carlin Bradley P.
Publication year - 2013
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1592
Subject(s) - bayesian network , bayesian probability , computer science , risk analysis (engineering) , meta analysis , process (computing) , management science , medicine , machine learning , artificial intelligence , engineering , operating system
The Drug Information Association Bayesian Scientific Working Group (BSWG) was formed in 2011 with a vision to ensure that Bayesian methods are well understood and broadly utilized for design and analysis and throughout the medical product development process, and to improve industrial, regulatory, and economic decision making. The group, composed of individuals from academia, industry, and regulatory, has as its mission to facilitate the appropriate use and contribute to the progress of Bayesian methodology. In this paper, the safety sub‐team of the BSWG explores the use of Bayesian methods when applied to drug safety meta‐analysis and network meta‐analysis. Guidance is presented on the conduct and reporting of such analyses. We also discuss different structural model assumptions and provide discussion on prior specification. The work is illustrated through a case study involving a network meta‐analysis related to the cardiovascular safety of non‐steroidal anti‐inflammatory drugs. Copyright © 2013 John Wiley & Sons, Ltd.