G-methods

G-Methods is a class of statistical techniques in longitudinal causal inference that encompasses marginal structural models, g-computation, and g-estimation, distinguished by its use of explicit modeling of treatment and weighting or standardization to adjust for time-varying confounders affected by prior exposure.

61

Publications

7.4K

Citations

195

Authors

130

Institutions

Publications per year

2017–2026

24

Authors

195

  1. AC
    Arthur Chatton

    Université de Tours, France

    5 publications · 204 citations

    5

    h-index

  2. ML
    Maxime Léger

    Inserm, France

    3 publications · 172 citations

    3

    h-index

  3. JA
    Jonathan A C Sterne

    University of Bristol, United Kingdom

    3 publications · 464 citations

    3

    h-index

  4. SR
    Sherri Rose

    Harvard University, United States

    3 publications · 1K citations

    3

    h-index

  5. AP
    Alexander P. Keil

    University of North Carolina at Chapel Hill, United States

    3 publications · 1.7K citations

    3

    h-index

1–5 of 195

Venues

  1. Statistics in Medicine

    7 publications · 617 citations

    7

    h-index

  2. American Journal of Epidemiology

    AJE · 5 publications · 1.1K citations

    5

    h-index

  3. The Journal of Physical Chemistry A

    3 publications · 93 citations

    3

    h-index

  4. Epidemiology

    3 publications · 2K citations

    3

    h-index

  5. BMC Medical Research Methodology

    3 publications · 126 citations

    3

    h-index

1–5