Observational Data Modeling

Observational Data Modeling is a methodological framework within clinical data modeling that distinguishes itself by structuring and standardizing data derived from real-world patient observations without active intervention, enabling the systematic representation of naturally occurring clinical phenomena for analysis and inference.

404

Publications

33.6K

Citations

1.5K

Authors

756

Institutions

Publications per year

2017–2026

177

Authors

1.5K

  1. MA
    Miguel A. Hernán

    Harvard University, United States

    5 publications · 346 citations

    5

    h-index

  2. JM
    James M. Robins

    Harvard University, United States

    5 publications · 700 citations

    5

    h-index

  3. IS
    Ilya Shpitser

    Johns Hopkins University, United States

    4 publications · 135 citations

    4

    h-index

  4. MJ
    Martijn J. Schuemie

    Erasmus MC, Netherlands

    4 publications · 139 citations

    4

    h-index

  5. NK
    Nathan Kallus

    Cornell University, United States

    4 publications · 112 citations

    4

    h-index

1–5 of 1.5K

Venues

  1. arXiv (Cornell University)

    Repository · 28 publications · 976 citations

    19

    h-index

  2. Statistics in Medicine

    11 publications · 2.6K citations

    11

    h-index

  3. 9

    h-index

  4. PubMed

    Repository · 8 publications · 183 citations

    8

    h-index

  5. 8

    h-index

1–5