Bayesian Dose Escalation
Bayesian Dose Escalation is a dose-finding methodology in clinical trial design that uses Bayesian statistical inference to sequentially update the probability of toxicity and efficacy outcomes, guiding dose escalation decisions based on accumulating data and prior knowledge.
123
Publications
7.1K
Citations
445
Authors
221
Institutions
Publications per year
2017–2026
44
Parents
123
Dose‐Finding Based on Efficacy–Toxicity Trade‐Offs
Peter F. Thall, John D. Cook · Biometrics · 2004 · 510 citations
1–5 of 123
445
The University of Texas MD Anderson Cancer Center, United States
18 publications · 1.6K citations
17
h-index
The University of Texas MD Anderson Cancer Center, United States
12 publications · 1.4K citations
12
h-index
11
h-index
10
h-index
9
h-index
1–5 of 445
221
The University of Texas MD Anderson Cancer Center
Houston, United States
30 h-index
89 publications
8.1K citations
Novartis (Switzerland)
Basel, Switzerland
10 h-index
25 publications
1.9K citations
University of Reading
Reading, United Kingdom
10 h-index
17 publications
653 citations
Lancaster University
Lancaster, United Kingdom
7 h-index
12 publications
225 citations
Memorial Sloan Kettering Cancer Center
New York, United States
7 h-index
9 publications
195 citations
Sarah Cannon
Nashville, United States
6 h-index
7 publications
149 citations
Massachusetts General Hospital
Boston, United States
5 h-index
7 publications
159 citations
Center for Global Development
Washington D.C., United States
5 h-index
8 publications
250 citations
1–8 of 221
20 publications · 1.7K citations
19
h-index
13 publications · 1.8K citations
12
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JBS · 10 publications · 389 citations
10
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11 publications · 312 citations
10
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10 publications · 537 citations
10
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