Missing Data Analysis

Missing Data Analysis is a methodological framework within statistical data analysis that specifically addresses the identification, characterization, and imputation of absent or unobserved data values in a dataset, distinguishing itself by its focus on maintaining data integrity and minimizing bias in inference across diverse data structures such as cross-sectional, longitudinal, time series, and experimental designs.

3K

Publications

406.4K

Citations

6.9K

Authors

2.3K

Institutions

Publications per year

2017–2026

904

Authors

6.9K

  1. RJ
    Roderick J. A. Little

    University of Michigan, United States

    48 publications · 42.1K citations

    35

    h-index

  2. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    42 publications · 14.4K citations

    32

    h-index

  3. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    38 publications · 24.8K citations

    30

    h-index

  4. DB
    Donald B. Rubin

    Harvard University, United States

    36 publications · 94.1K citations

    29

    h-index

  5. GM
    Geert Molenberghs

    Hasselt University, Belgium

    48 publications · 5K citations

    29

    h-index

1–5 of 6.9K

Venues

  1. Journal of the American Statistical Association

    137 publications · 45.1K citations

    62

    h-index

  2. Statistics in Medicine

    146 publications · 21.5K citations

    53

    h-index

  3. Biometrics

    79 publications · 5K citations

    40

    h-index

  4. BMC Medical Research Methodology

    51 publications · 7.9K citations

    34

    h-index

  5. Journal of Clinical Epidemiology

    32 publications · 7.9K citations

    29

    h-index

1–5