Missing Data Impact Analysis

Missing Data Impact Analysis is a methodological framework within the domain of Sample Ratio Mismatch Detection that systematically evaluates the extent to which missing data distorts observed sample ratios and biases inferential outcomes in experimental studies.

74

Publications

6.7K

Citations

274

Authors

197

Institutions

Publications per year

2017–2026

21

Authors

274

  1. RP
    Ryan P. McLynn

    Yale University, United States

    2 publications · 87 citations

    2

    h-index

  2. DD
    Daniel D. Bohl

    Rush University Medical Center, United States

    2 publications · 87 citations

    2

    h-index

  3. AM
    Adam M. Lukasiewicz

    Yale University, United States

    2 publications · 87 citations

    2

    h-index

  4. AM
    Andre M. Samuel

    Yale University, United States

    2 publications · 87 citations

    2

    h-index

  5. JN
    Jonathan N. Grauer

    Yale University, United States

    2 publications · 87 citations

    2

    h-index

1–5 of 274

Venues

  1. Statistics in Medicine

    5 publications · 395 citations

    5

    h-index

  2. Pharmaceutical Statistics

    2 publications · 27 citations

    2

    h-index

  3. BMJ Open

    2 publications · 84 citations

    2

    h-index

  4. BMJ

    2 publications · 510 citations

    2

    h-index

  5. Biometrical Journal

    2 publications · 100 citations

    2

    h-index

1–5