Literature DB >> 24027093

The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group.

Fanni Natanegara1, Beat Neuenschwander, John W Seaman, Nelson Kinnersley, Cory R Heilmann, David Ohlssen, George Rochester.   

Abstract

Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Bayesian; education; medical product development; survey

Mesh:

Year:  2013        PMID: 24027093     DOI: 10.1002/pst.1595

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  3 in total

1.  Hybrid-control arm construction using historical trial data for an early-phase, randomized controlled trial in metastatic colorectal cancer.

Authors:  Chen Li; Ana Ferro; Shivani K Mhatre; Danny Lu; Marcus Lawrance; Xiao Li; Shi Li; Simon Allen; Jayesh Desai; Marwan Fakih; Michael Cecchini; Katrina S Pedersen; Tae You Kim; Irmarie Reyes-Rivera; Neil H Segal; Christelle Lenain
Journal:  Commun Med (Lond)       Date:  2022-07-15

2.  Why are not There More Bayesian Clinical Trials? Perceived Barriers and Educational Preferences Among Medical Researchers Involved in Drug Development.

Authors:  Jennifer Clark; Natalia Muhlemann; Fanni Natanegara; Andrew Hartley; Deborah Wenkert; Fei Wang; Frank E Harrell; Ross Bray
Journal:  Ther Innov Regul Sci       Date:  2022-01-03       Impact factor: 1.778

3.  Performance of Hamiltonian Monte Carlo and No-U-Turn Sampler for estimating genetic parameters and breeding values.

Authors:  Motohide Nishio; Aisaku Arakawa
Journal:  Genet Sel Evol       Date:  2019-12-10       Impact factor: 4.297

  3 in total

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