Missing Data Adjustment

Missing Data Adjustment is a statistical method designed to correct for biases introduced by incomplete data within propensity score analyses by incorporating techniques such as multiple imputation or inverse probability weighting to ensure valid causal inferences.

391

Publications

31.6K

Citations

1.1K

Authors

533

Institutions

Publications per year

2017–2026

105

Authors

1.1K

  1. IR
    Ian R. White

    MRC Biostatistics Unit, United Kingdom

    13 publications · 3.7K citations

    13

    h-index

  2. RJ
    Roderick J. A. Little

    University of Michigan, United States

    12 publications · 1.7K citations

    12

    h-index

  3. JR
    James R. Carpenter

    London School of Hygiene & Tropical Medicine, United Kingdom

    7 publications · 2K citations

    7

    h-index

  4. DB
    Donald B. Rubin

    Harvard University, United States

    6 publications · 587 citations

    6

    h-index

  5. KJ
    Katherine J. Lee

    Murdoch Children's Research Institute, Australia

    5 publications · 303 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Statistics in Medicine

    42 publications · 3.9K citations

    27

    h-index

  2. BMC Medical Research Methodology

    18 publications · 3.5K citations

    15

    h-index

  3. Journal of the American Statistical Association

    15 publications · 1.2K citations

    15

    h-index

  4. American Journal of Epidemiology

    AJE · 12 publications · 1.2K citations

    13

    h-index

  5. Biometrics

    14 publications · 591 citations

    12

    h-index

1–5