Bayesian Adaptive Randomization

Bayesian Adaptive Randomization is a statistical method for dynamically allocating treatments in clinical trials that uses Bayesian updating to modify assignment probabilities based on accumulating efficacy and safety data, thereby increasing the likelihood of assigning participants to better-performing interventions while maintaining randomization.

62

Publications

4.5K

Citations

519

Authors

269

Institutions

Publications per year

2017–2026

24

Authors

519

  1. LT
    Lorenzo Trippa

    Dana-Farber Cancer Institute, United States

    10 publications · 538 citations

    10

    h-index

  2. DA
    Donald A. Berry

    The University of Texas MD Anderson Cancer Center, United States

    8 publications · 891 citations

    8

    h-index

  3. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    7 publications · 1K citations

    7

    h-index

  4. JK
    J. Kyle Wathen

    The University of Texas MD Anderson Cancer Center, United States

    5 publications · 954 citations

    5

    h-index

  5. HC
    Howard Colman

    The University of Texas MD Anderson Cancer Center, United States

    5 publications · 149 citations

    5

    h-index

1–5 of 519

Venues

  1. Journal of Clinical Oncology

    8 publications · 1.5K citations

    8

    h-index

  2. Clinical Trials

    7 publications · 503 citations

    7

    h-index

  3. Statistics in Medicine

    5 publications · 332 citations

    5

    h-index

  4. Journal of Biopharmaceutical Statistics

    JBS · 3 publications · 106 citations

    3

    h-index

  5. Biometrics

    2 publications · 31 citations

    2

    h-index

1–5