Bayesian Response-adaptive Randomization

Bayesian Response-Adaptive Randomization is a clinical trial design methodology that dynamically allocates patients to treatment arms based on accumulating efficacy and safety data, using Bayesian statistical models to update probability assignments and optimize allocation toward more promising interventions while maintaining randomization.

39

Publications

2.1K

Citations

373

Authors

197

Institutions

Publications per year

2017–2026

20

Authors

373

  1. LT
    Lorenzo Trippa

    Dana-Farber Cancer Institute, United States

    9 publications · 440 citations

    9

    h-index

  2. SB
    Scott Berry

    Berry & Associates (United States), United States

    6 publications · 1K citations

    6

    h-index

  3. MC
    Matteo Cellamare

    Dana-Farber Cancer Institute, United States

    4 publications · 140 citations

    4

    h-index

  4. SV
    Steffen Ventz

    Dana-Farber Cancer Institute, United States

    3 publications · 98 citations

    3

    h-index

  5. DA
    Donald A. Berry

    The University of Texas MD Anderson Cancer Center, United States

    3 publications · 882 citations

    3

    h-index

1–5 of 373

Venues

  1. Clinical Trials

    4 publications · 193 citations

    4

    h-index

  2. Statistics in Medicine

    4 publications · 201 citations

    4

    h-index

  3. Journal of Clinical Oncology

    3 publications · 85 citations

    3

    h-index

  4. Journal of Biopharmaceutical Statistics

    JBS · 2 publications · 28 citations

    2

    h-index

  5. Statistical Methods in Medical Research

    2 publications · 75 citations

    2

    h-index

1–5