Bayesian Exposure-response Modeling

Bayesian Exposure-Response Modeling is a statistical modeling framework that belongs to the class of exposure-response modeling approaches and is distinguished by its incorporation of prior knowledge and probabilistic inference through Bayesian methods to quantify the relationship between exposure levels and health outcomes.

50

Publications

3.8K

Citations

222

Authors

118

Institutions

Publications per year

2017–2026

12

Authors

222

  1. GR
    Gurumurthy Ramachandran

    University of Minnesota, United States

    3 publications · 157 citations

    3

    h-index

  2. BC
    Birgit Claus Henn

    Boston University, United States

    2 publications · 1.8K citations

    2

    h-index

  3. RO
    Robert O. Wright

    Icahn School of Medicine at Mount Sinai, United States

    2 publications · 1.8K citations

    2

    h-index

  4. HK
    Hans Kromhout

    Utrecht University, Netherlands

    2 publications · 81 citations

    2

    h-index

  5. NW
    Nicholas Warren

    UConn Health, United States

    2 publications · 81 citations

    2

    h-index

1–5 of 222

Venues

  1. Statistics in Medicine

    4 publications · 131 citations

    4

    h-index

  2. Health Physics

    2 publications · 31 citations

    2

    h-index

  3. Regulatory Toxicology and Pharmacology

    RTP · 2 publications · 36 citations

    2

    h-index

  4. Radiation Protection Dosimetry

    2 publications · 37 citations

    2

    h-index

  5. Annals of Work Exposures and Health

    2 publications · 32 citations

    2

    h-index

1–5