Confounder Data Management

Confounder Data Management is a data management framework designed to identify, assess, and mitigate the influence of confounding variables in observational and experimental studies to ensure valid causal inferences.

9

Publications

303

Citations

47

Authors

28

Institutions

Publications per year

2017–2026

5

Authors

47

  1. SS
    Sebastian Schneeweiß

    Brigham and Women's Hospital, United States

    3 publications · 85 citations

    3

    h-index

  2. RJ
    Robert J. Glynn

    Brigham and Women's Hospital, United States

    2 publications · 57 citations

    2

    h-index

  3. JJ
    Joshua J. Gagne

    Brigham and Women's Hospital, United States

    2 publications · 55 citations

    2

    h-index

  4. AP
    Adolf Pfefferbaum

    Stanford University, United States

    1 publications · 14 citations

    1

    h-index

  5. WH
    Wing Hung Wong

    Stanford University, United States

    1 publications · 59 citations

    1

    h-index

1–5 of 47

Venues

  1. Pharmacoepidemiology and Drug Safety

    2 publications · 61 citations

    2

    h-index

  2. PLoS Computational Biology

    1 publications · 38 citations

    1

    h-index

  3. Epidemiology

    1 publications · 37 citations

    1

    h-index

  4. Genome biology

    1 publications · 37 citations

    1

    h-index

  5. Journal of Clinical Epidemiology

    1 publications · 27 citations

    1

    h-index

1–5