Missing Data Methods

Missing Data Methods is a statistical methodology within epidemiologic research that addresses the systematic absence of data values by providing principled approaches to estimate, impute, or account for missing observations under explicit assumptions about their mechanism.

7.1K

Publications

733.1K

Citations

17.2K

Authors

4.4K

Institutions

Publications per year

2017–2026

2.3K

Authors

17.2K

  1. DB
    Donald B. Rubin

    Harvard University, United States

    64 publications · 92.1K citations

    47

    h-index

  2. RJ
    Roderick J. A. Little

    University of Michigan, United States

    74 publications · 46.7K citations

    45

    h-index

  3. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    63 publications · 28.9K citations

    44

    h-index

  4. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    59 publications · 16.2K citations

    44

    h-index

  5. GM
    Geert Molenberghs

    Hasselt University, Belgium

    76 publications · 6.5K citations

    36

    h-index

1–5 of 17.2K

Venues

  1. Journal of the American Statistical Association

    284 publications · 66.7K citations

    94

    h-index

  2. Statistics in Medicine

    354 publications · 35.2K citations

    75

    h-index

  3. Biometrics

    206 publications · 27.8K citations

    61

    h-index

  4. Biometrika

    128 publications · 10.2K citations

    52

    h-index

  5. BMC Medical Research Methodology

    87 publications · 10.7K citations

    41

    h-index

1–5