Adaptive Sample Size Planning
Adaptive Sample Size Planning is a Bayesian methodology for determining sample size that dynamically adjusts recruitment or experimental expansion based on accumulating data and updated posterior or predictive inferences, distinguishing itself from fixed sample approaches by incorporating interim evidence to optimize statistical power, precision, or decision criteria within a sequential learning framework.
12
Publications
992
Citations
49
Authors
29
Institutions
Publications per year
2017–2026
7
12
Sample size planning for classification models
Claudia Beleites, Ute Neugebauer, Thomas Bocklitz et al. · Analytica Chimica Acta · 2012 · 462 citations · Full text
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University Hospitals Coventry and Warwickshire NHS Trust, United Kingdom
1 publications · 17 citations
1
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1
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Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States
1 publications · 12 citations
1
h-index
1–5 of 49
29

Cornell University
Ithaca, United States
2 h-index
2 publications
242 citations

University of Bath
Bath, United Kingdom
2 h-index
2 publications
242 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
1 h-index
1 publications
25 citations
University Hospitals Coventry and Warwickshire NHS Trust
Coventry, United Kingdom
1 h-index
5 publications
85 citations
Rikkyo University
Tokyo, Japan
1 h-index
1 publications
43 citations
Presbyterian Hospital
Albuquerque, United States
1 h-index
3 publications
171 citations
George Mason University
Fairfax, United States
1 h-index
1 publications
12 citations
United States Food and Drug Administration
Silver Spring, United States
1 h-index
3 publications
210 citations
1–8 of 29
2 publications · 242 citations
2
h-index
1 publications · 12 citations
1
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1 publications · 57 citations
1
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1 publications · 25 citations
1
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1 publications · 462 citations
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