Marginal Mean Models

Marginal Mean Models is a statistical modeling framework that belongs to the class of marginal structural models and is distinguished by its focus on estimating population-averaged effects of time-varying treatments while accounting for time-dependent confounding through inverse probability weighting.

120

Publications

11.2K

Citations

284

Authors

186

Institutions

Publications per year

2017–2026

27

Authors

284

  1. JM
    James M. Robins

    Harvard University, United States

    9 publications · 2.2K citations

    9

    h-index

  2. GM
    Garrett M. Fitzmaurice

    Harvard University, United States

    8 publications · 755 citations

    8

    h-index

  3. JS
    John S. Preisser

    University of North Carolina at Chapel Hill, United States

    8 publications · 404 citations

    8

    h-index

  4. NM
    Nan M. Laird

    Harvard University, United States

    7 publications · 743 citations

    7

    h-index

  5. AR
    Andrea Rotnitzky

    Harvard University, United States

    6 publications · 1.6K citations

    6

    h-index

1–5 of 284

Venues

  1. Statistics in Medicine

    16 publications · 1.1K citations

    15

    h-index

  2. Journal of the American Statistical Association

    13 publications · 2.4K citations

    12

    h-index

  3. Biometrics

    12 publications · 730 citations

    11

    h-index

  4. Statistical Methods in Medical Research

    4 publications · 116 citations

    4

    h-index

  5. Biostatistics

    4 publications · 122 citations

    4

    h-index

1–5