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

Authors

445

  1. YY
    Ying Yuan

    The University of Texas MD Anderson Cancer Center, United States

    18 publications · 1.6K citations

    17

    h-index

  2. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    12 publications · 1.4K citations

    12

    h-index

  3. JW
    John Whitehead

    University of Reading, United Kingdom

    11 publications · 451 citations

    11

    h-index

  4. MT
    Mourad Tighiouart

    Emory University, United States

    10 publications · 393 citations

    10

    h-index

  5. AR
    André Rogatko

    Fox Chase Cancer Center, United States

    9 publications · 392 citations

    9

    h-index

1–5 of 445

Venues

  1. Statistics in Medicine

    20 publications · 1.7K citations

    19

    h-index

  2. Biometrics

    13 publications · 1.8K citations

    12

    h-index

  3. Journal of Biopharmaceutical Statistics

    JBS · 10 publications · 389 citations

    10

    h-index

  4. Pharmaceutical Statistics

    11 publications · 312 citations

    10

    h-index

  5. 10

    h-index

1–5