Bayesian Stopping Rules
Bayesian Stopping Rules is a statistical decision framework used in clinical trial design that specifies criteria for terminating a study based on posterior or predictive probabilities, distinguishing it from frequentist approaches by incorporating prior information and allowing sequential evaluation of evidence in favor of a hypothesis.
96
Publications
7.7K
Citations
205
Authors
137
Institutions
Publications per year
2017–2026
12
Parents
96
A Practical Bayesian Framework for Backpropagation Networks
David Mackay · Neural Computation · 1992 · 2.9K citations
Optional stopping: No problem for Bayesians
Jeffrey N. Rouder · Psychonomic Bulletin & Review · 2014 · 531 citations
1–5 of 96
205
The University of Texas MD Anderson Cancer Center, United States
6 publications · 415 citations
6
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The University of Texas MD Anderson Cancer Center, United States
4 publications · 462 citations
4
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4
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3
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3
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1–5 of 205
137
The University of Texas MD Anderson Cancer Center
Houston, United States
10 h-index
16 publications
1.5K citations
University of California, Irvine
Irvine, United States
6 h-index
6 publications
222 citations
University of Washington
Seattle, United States
4 h-index
4 publications
171 citations
Decision Sciences (United States)
Fort Walton Beach, United States
4 h-index
7 publications
286 citations
Duke University
Durham, United States
3 h-index
5 publications
216 citations
University of Michigan
Ann Arbor, United States
3 h-index
8 publications
90 citations
Harbor–UCLA Medical Center
Torrance, United States
3 h-index
4 publications
208 citations

University of Minnesota
Minneapolis, United States
3 h-index
3 publications
153 citations
1–8 of 137
16 publications · 1.3K citations
15
h-index
8 publications · 375 citations
8
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4 publications · 79 citations
4
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3 publications · 266 citations
3
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2 publications · 67 citations
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