Bayesian Adaptive Trials

Bayesian Adaptive Trials is a statistical framework for sequential experimentation that uses Bayesian inference to dynamically modify trial parameters based on accumulating data, thereby distinguishing it from fixed-design and frequentist adaptive approaches.

99

Publications

5K

Citations

539

Authors

287

Institutions

Publications per year

2017–2026

45

Variants

Bayesian Adaptive Clinical Trials

Authors

539

  1. KB
    Kristine Broglio

    The University of Texas MD Anderson Cancer Center, United States

    8 publications · 496 citations

    8

    h-index

  2. LT
    Lorenzo Trippa

    Dana-Farber Cancer Institute, United States

    7 publications · 427 citations

    7

    h-index

  3. YY
    Ying Yuan

    The University of Texas MD Anderson Cancer Center, United States

    7 publications · 425 citations

    7

    h-index

  4. MT
    Mourad Tighiouart

    Emory University, United States

    6 publications · 402 citations

    6

    h-index

  5. SB
    Scott Berry

    Berry & Associates (United States), United States

    5 publications · 408 citations

    5

    h-index

1–5 of 539

Venues

  1. Statistics in Medicine

    10 publications · 391 citations

    10

    h-index

  2. Clinical Trials

    7 publications · 718 citations

    7

    h-index

  3. BMC Medical Research Methodology

    5 publications · 191 citations

    5

    h-index

  4. Journal of Biopharmaceutical Statistics

    JBS · 5 publications · 181 citations

    5

    h-index

  5. 4

    h-index

1–5