Data Cut-off Rules

Data Cut-Off Rules is a formal methodological framework that specifies predetermined conditions or thresholds for excluding or truncating data points from a dataset based on systematic criteria such as time, quality, relevance, or measurement boundaries.

47

Publications

4.6K

Citations

733

Authors

363

Institutions

Publications per year

2017–2026

35

Variants

Data Cut-off Criteria

Authors

733

  1. KD
    Keith D. Wilner

    Pfizer (United States), United States

    3 publications · 944 citations

    3

    h-index

  2. MA
    Mark A. Socinski

    University of North Carolina at Chapel Hill, United States

    3 publications · 422 citations

    3

    h-index

  3. DR
    D. Ross Camidge

    University of Colorado Denver, United States

    3 publications · 1.1K citations

    3

    h-index

  4. MV
    Marileila Varella‐Garcia

    University of Colorado Denver, United States

    2 publications · 833 citations

    2

    h-index

  5. CW
    Christian Werner

    Saarland University, Germany

    2 publications · 83 citations

    2

    h-index

1–5 of 733

Venues

  1. Journal of Clinical Oncology

    4 publications · 447 citations

    4

    h-index

  2. Annals of Oncology

    3 publications · 1.6K citations

    3

    h-index

  3. Biometrika

    2 publications · 281 citations

    2

    h-index

  4. Multiple Sclerosis Journal

    MSJ · 2 publications · 94 citations

    2

    h-index

  5. Amyloid

    1 publications · 24 citations

    1

    h-index

1–5