Bayesian evidence synthesis for exploring generalizability of treatment effects: a case study of combining randomized and non-randomized results in diabetes

2015 ◽  
Vol 35 (10) ◽  
pp. 1654-1675 ◽  
Author(s):  
Pablo E. Verde ◽  
Christian Ohmann ◽  
Stephan Morbach ◽  
Andrea Icks
Keyword(s):  
Treatment Effects ◽  
Evidence Synthesis ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis

Bayesian Evidence Synthesis to Estimate Subnational TB Incidence: An Application in Brazil

2019 ◽  
Author(s):  
Melanie Chitwood ◽  
Daniele M. Pelissari ◽  
Gabriela Drummond Marques da Silva ◽  
Patricia Bartholomay ◽  
Marli Souza Rocha ◽  
...  
Keyword(s):  
Evidence Synthesis ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis

scholarly journals Direct benefit of vaccinating boys along with girls against oncogenic human papillomavirus: bayesian evidence synthesis

BMJ ◽  
2015 ◽  
Vol 350 (may12 7) ◽  
pp. h2016-h2016 ◽  
Author(s):  
J. A. Bogaards ◽  
J. Wallinga ◽  
R. H. Brakenhoff ◽  
C. J. L. M. Meijer ◽  
J. Berkhof
Keyword(s):  
Human Papillomavirus ◽  
Evidence Synthesis ◽  
Direct Benefit ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis

scholarly journals Bayesian Evidence Synthesis of Safety Data: A Robust Option?

2013 ◽  
Vol 16 (7) ◽  
pp. A592
Author(s):  
B. Amzal ◽  
M. Nikodem
Keyword(s):  
Evidence Synthesis ◽  
Safety Data ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis

Bayesian Evidence Synthesis Can Reconcile Seemingly Inconsistent Results

2016 ◽  
Vol 27 (7) ◽  
pp. 1043-1046 ◽  
Author(s):  
Benjamin Scheibehenne ◽  
Tahira Jamil ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Evidence Synthesis ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis

Bayesian evidence synthesis to extrapolate survival estimates in cost-effectiveness studies

2006 ◽  
Vol 25 (11) ◽  
pp. 1960-1975 ◽  
Author(s):  
N. Demiris ◽  
L. D. Sharples
Keyword(s):  
Cost Effectiveness ◽  
Evidence Synthesis ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis ◽  
Effectiveness Studies

scholarly journals Estimating the burden of A(H1N1)pdm09 influenza in Finland during two seasons

2013 ◽  
Vol 142 (5) ◽  
pp. 964-974 ◽  
Author(s):  
M. SHUBIN ◽  
M. VIRTANEN ◽  
S. TOIKKANEN ◽  
O. LYYTIKÄINEN ◽  
K. AURANEN
Keyword(s):  
Infectious Disease ◽  
Surveillance System ◽  
Prior Information ◽  
Evidence Synthesis ◽  
Hospitalization Rate ◽  
Disease Registry ◽  
Total Incidence ◽  
True Number ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis

SUMMARYIn Finland, the pandemic influenza virus A(H1N1)pdm09 was the dominant influenza strain during the pandemic season in 2009/2010 and presented alongside other influenza types during the 2010/2011 season. The true number of infected individuals is unknown, as surveillance missed a large portion of mild infections. We applied Bayesian evidence synthesis, combining available data from the national infectious disease registry with an ascertainment model and prior information on A(H1N1)pdm09 influenza and the surveillance system, to estimate the total incidence and hospitalization rate of A(H1N1)pdm09 infection. The estimated numbers of A(H1N1)pdm09 infections in Finland were 211 000 (4% of the population) in the 2009/2010 pandemic season and 53 000 (1% of the population) during the 2010/2011 season. Altogether, 1·1% of infected individuals were hospitalized. Only 1 infection per 25 was ascertained.

scholarly journals Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools

2021 ◽  
Vol 100 (19) ◽  
Author(s):  
Sebastian Weber ◽  
Yue Li ◽  
John W. Seaman III ◽  
Tomoyuki Kakizume ◽  
Heinz Schmidli
Keyword(s):  
Evidence Synthesis ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis

scholarly journals A Review of Applications of the Bayes Factor in Psychological Research

2020 ◽  
Author(s):  
Daniel W. Heck ◽  
Udo Boehm ◽  
Florian Böing-Messing ◽  
Paul - Christian Bürkner ◽  
Koen Derks ◽  
...  
Keyword(s):  
Bayes Factor ◽  
Evidence Synthesis ◽  
Model Averaging ◽  
Psychological Research ◽  
Bayesian Variable Selection ◽  
Future Research ◽  
Steady Increase ◽  
Bayesian Evidence ◽  
Bayesian Evidence Synthesis ◽  
Hypothesis Evaluation

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The paper is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines.

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