Adaptive Sample Size Planning

Adaptive Sample Size Planning is a Bayesian methodology for determining sample size that dynamically adjusts recruitment or experimental expansion based on accumulating data and updated posterior or predictive inferences, distinguishing itself from fixed sample approaches by incorporating interim evidence to optimize statistical power, precision, or decision criteria within a sequential learning framework.

12

Publications

992

Citations

49

Authors

29

Institutions

Publications per year

2017–2026

7

Authors

49

  1. CJ
    Christopher Jennison

    University of Bath, United Kingdom

    2 publications · 242 citations

    2

    h-index

  2. BW
    Bruce W. Turnbull

    Cornell University, United States

    2 publications · 242 citations

    2

    h-index

  3. JF
    Josephine Fox

    University Hospitals Coventry and Warwickshire NHS Trust, United Kingdom

    1 publications · 17 citations

    1

    h-index

  4. HS
    Howard S. Bush

    St. Luke's Episcopal Hospital, United States

    1 publications · 17 citations

    1

    h-index

  5. AL
    Aiyi Liu

    Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States

    1 publications · 12 citations

    1

    h-index

1–5 of 49

Venues

  1. Statistics in Medicine

    2 publications · 242 citations

    2

    h-index

  2. Biostatistics

    1 publications · 12 citations

    1

    h-index

  3. Critical Care Medicine

    1 publications · 57 citations

    1

    h-index

  4. Prevention Science

    1 publications · 25 citations

    1

    h-index

  5. Analytica Chimica Acta

    1 publications · 462 citations

    1

    h-index

1–5