Adaptive Decision Rules

Adaptive Decision Rules is a methodological framework in adaptive experimental design that specifies pre-defined criteria for modifying study parameters during the trial based on accumulating data, thereby distinguishing it from fixed decision protocols by enabling dynamic, data-driven adjustments to optimize trial efficiency and validity.

86

Publications

5.5K

Citations

230

Authors

147

Institutions

Publications per year

2017–2026

16

Authors

230

  1. SH
    Seppo Honkapohja

    University of Oregon, United States

    4 publications · 481 citations

    4

    h-index

  2. GW
    George W. Evans

    University of Oregon, United States

    4 publications · 481 citations

    4

    h-index

  3. AD
    Antonio De Maio

    University of Naples Federico II, Italy

    3 publications · 92 citations

    3

    h-index

  4. DO
    Danilo Orlando

    University Niccolò Cusano, Italy

    3 publications · 92 citations

    3

    h-index

  5. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    2 publications · 86 citations

    2

    h-index

1–5 of 230

Venues

  1. The American Naturalist

    AN · 3 publications · 139 citations

    3

    h-index

  2. Statistics in Medicine

    2 publications · 77 citations

    2

    h-index

  3. Journal of Biopharmaceutical Statistics

    JBS · 2 publications · 27 citations

    2

    h-index

  4. Ecology

    2 publications · 384 citations

    2

    h-index

  5. IET Radar Sonar & Navigation

    2 publications · 57 citations

    2

    h-index

1–5