Bayesian Adaptive Design

Bayesian Adaptive Design is a type of clinical trial design that uses Bayesian statistical methods to sequentially modify trial procedures or hypotheses based on accumulating data, allowing for flexible decision-making while maintaining rigorous inferential validity.

251

Publications

14.9K

Citations

826

Authors

441

Institutions

Publications per year

2017–2026

106

Authors

826

  1. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    21 publications · 859 citations

    19

    h-index

  2. YY
    Ying Yuan

    The University of Texas MD Anderson Cancer Center, United States

    23 publications · 1.6K citations

    18

    h-index

  3. DA
    Donald A. Berry

    The University of Texas MD Anderson Cancer Center, United States

    12 publications · 720 citations

    12

    h-index

  4. SB
    Scott Berry

    Berry & Associates (United States), United States

    11 publications · 654 citations

    11

    h-index

  5. GY
    Guosheng Yin

    University of Hong Kong, Hong Kong SAR China

    9 publications · 742 citations

    9

    h-index

1–5 of 826

Venues

  1. Statistics in Medicine

    37 publications · 1.6K citations

    23

    h-index

  2. Biometrics

    18 publications · 991 citations

    17

    h-index

  3. Clinical Trials

    15 publications · 1.1K citations

    15

    h-index

  4. Journal of Biopharmaceutical Statistics

    JBS · 15 publications · 455 citations

    13

    h-index

  5. Pharmaceutical Statistics

    11 publications · 352 citations

    11

    h-index

1–5