Probabilistic Decision Modeling

Probabilistic Decision Modeling is a formal analytic framework within decision science that characterizes uncertainty in model inputs through probability distributions and propagates this uncertainty via stochastic simulation to quantify the confidence in cost-effectiveness decisions, distinguishing it from deterministic models by explicitly accounting for parameter and model uncertainty in incremental cost-effectiveness analysis.

320

Publications

18.7K

Citations

880

Authors

444

Institutions

Publications per year

2017–2026

71

Authors

880

  1. AB
    Andrew Briggs

    University of Glasgow, United Kingdom

    6 publications · 831 citations

    6

    h-index

  2. AE
    A. E. Ades

    University of Bristol, United Kingdom

    5 publications · 673 citations

    5

    h-index

  3. KC
    Karl Claxton

    University of York, United Kingdom

    5 publications · 653 citations

    5

    h-index

  4. MS
    Mark Sculpher

    University of York, United Kingdom

    4 publications · 224 citations

    4

    h-index

  5. AJ
    Alex J. Sutton

    University of Leicester, United Kingdom

    4 publications · 262 citations

    4

    h-index

1–5 of 880

Venues

  1. Medical Decision Making

    MDM · 20 publications · 2.3K citations

    17

    h-index

  2. Management Science

    7 publications · 654 citations

    7

    h-index

  3. Health Economics

    6 publications · 672 citations

    6

    h-index

  4. Operations Research

    5 publications · 1.1K citations

    5

    h-index

  5. PubMed

    Repository · 4 publications · 141 citations

    4

    h-index

1–5