Stopping Boundaries

Stopping Boundaries is a sequential analysis framework that specifies predetermined decision rules for terminating an experiment based on cumulative evidence, distinguishing it from ad hoc or fixed-sample designs by enabling early stopping while controlling error rates.

94

Publications

2.9K

Citations

185

Authors

132

Institutions

Publications per year

2017–2026

16

Authors

185

  1. RA
    Rebecca A. Betensky

    Harvard University, United States

    4 publications · 79 citations

    4

    h-index

  2. TL
    Tze Leung Lai

    Stanford University, United States

    3 publications · 145 citations

    3

    h-index

  3. WJ
    William J. Hall

    University of Rochester, United States

    3 publications · 69 citations

    3

    h-index

  4. DA
    Donald A. Berry

    The University of Texas MD Anderson Cancer Center, United States

    2 publications · 118 citations

    2

    h-index

  5. DA
    Douglas A. Fitts

    University of Washington, United States

    2 publications · 55 citations

    2

    h-index

1–5 of 185

Venues

  1. Biometrics

    7 publications · 311 citations

    7

    h-index

  2. Statistics in Medicine

    7 publications · 190 citations

    7

    h-index

  3. The Annals of Statistics

    3 publications · 129 citations

    3

    h-index

  4. Sequential Analysis

    3 publications · 64 citations

    3

    h-index

  5. Biometrika

    3 publications · 85 citations

    3

    h-index

1–5