Hierarchical Bayesian Meta-analysis

Hierarchical Bayesian Meta-Analysis is a Bayesian meta-analytic model that belongs to the class of hierarchical models by structuring parameters in multiple levels to account for between-study heterogeneity and within-study variability, while distinguishing itself through the incorporation of prior distributions and joint probabilistic modeling of effect sizes and hyperparameters across studies.

105

Publications

9.5K

Citations

1.3K

Authors

250

Institutions

Publications per year

2017–2026

55

Authors

1.3K

  1. YH
    Young Ho Lee

    Korea University, South Korea

    5 publications · 126 citations

    5

    h-index

  2. AE
    A. E. Ades

    University of Bristol, United Kingdom

    4 publications · 3.3K citations

    4

    h-index

  3. HC
    Haitao Chu

    University of Minnesota, United States

    4 publications · 104 citations

    4

    h-index

  4. KR
    Keith R. Abrams

    University of Leicester, United Kingdom

    4 publications · 210 citations

    4

    h-index

  5. DS
    David Simons

    University College London, United Kingdom

    3 publications · 66 citations

    3

    h-index

1–5 of 1.3K

Venues

  1. Statistics in Medicine

    9 publications · 3K citations

    9

    h-index

  2. 5

    h-index

  3. Research Synthesis Methods

    4 publications · 154 citations

    4

    h-index

  4. Biometrics

    4 publications · 201 citations

    4

    h-index

  5. Statistical Methods in Medical Research

    3 publications · 105 citations

    3

    h-index

1–5