Bayesian Stopping Rules

Bayesian Stopping Rules is a statistical decision framework used in clinical trial design that specifies criteria for terminating a study based on posterior or predictive probabilities, distinguishing it from frequentist approaches by incorporating prior information and allowing sequential evaluation of evidence in favor of a hypothesis.

96

Publications

7.7K

Citations

205

Authors

137

Institutions

Publications per year

2017–2026

12

Authors

205

  1. DA
    Donald A. Berry

    The University of Texas MD Anderson Cancer Center, United States

    6 publications · 415 citations

    6

    h-index

  2. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    4 publications · 462 citations

    4

    h-index

  3. SS
    Scott S. Emerson

    University of Washington, United States

    4 publications · 151 citations

    4

    h-index

  4. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    3 publications · 101 citations

    3

    h-index

  5. JM
    John M. Kittelson

    University of Colorado Denver, United States

    3 publications · 136 citations

    3

    h-index

1–5 of 205

Venues

  1. Statistics in Medicine

    16 publications · 1.3K citations

    15

    h-index

  2. 8

    h-index

  3. Sequential Analysis

    4 publications · 79 citations

    4

    h-index

  4. Clinical Trials

    3 publications · 266 citations

    3

    h-index

  5. 2

    h-index

1–5