Bayesian Sample Size Determination
Bayesian Sample Size Determination is a statistical methodology that belongs to the class of sample size determination techniques, distinguished by its incorporation of prior information and probabilistic inference under a Bayesian framework to ascertain the minimum sample size required for achieving prespecified levels of posterior precision, power, or decision-theoretic utility in parameter estimation or hypothesis testing.
112
Publications
4.4K
Citations
194
Authors
129
Institutions
Publications per year
2017–2026
12
Parents
Children
Adaptive Randomization DesignAdaptive Sample Size PlanningBayesian AssuranceBayesian Decision-theoretic Sample SizeBayesian Nonparametric Sample SizeBayesian Precision-based Sample SizeBinary Outcome Sample SizeClinical Trial Design Under Bayesian FrameworkContinuous Outcome Sample SizeDose-finding Trial Design
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129

Sapienza University of Rome
Rome, Italy
10 h-index
18 publications
595 citations

McGill University
Montreal, Canada
10 h-index
20 publications
1.5K citations
Montreal General Hospital
Montreal, Canada
7 h-index
16 publications
1.5K citations

Baylor University
Waco, United States
7 h-index
12 publications
165 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
5 h-index
10 publications
346 citations
University of Connecticut
Storrs, United States
5 h-index
8 publications
862 citations
University of Kentucky
Lexington, United States
4 h-index
4 publications
68 citations
Inserm
Paris, France
3 h-index
3 publications
54 citations
1–8 of 129
20 publications · 1K citations
16
h-index
10 publications · 446 citations
10
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7 publications · 432 citations
7
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4 publications · 280 citations
4
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4 publications · 197 citations
4
h-index
1–5