Mixed-effects Logistic Regression

Mixed-Effects Logistic Regression is a statistical model within the class of generalized linear mixed models that extends standard logistic regression by incorporating both fixed and random effects to account for hierarchical or clustered data structures in binary response variables.

1K

Publications

39.4K

Citations

7.3K

Authors

2.2K

Institutions

Publications per year

2017–2026

665

Variants

Multilevel Logistic Regression

Authors

7.3K

  1. GA
    Getayeneh Antehunegn Tesema

    University of Gondar, Ethiopia

    8 publications · 335 citations

    8

    h-index

  2. PC
    Patrizia Carrieri

    Inserm, France

    8 publications · 166 citations

    7

    h-index

  3. BA
    Beth A. Reboussin

    Wake Forest University, United States

    6 publications · 233 citations

    6

    h-index

  4. AB
    Achamyeleh Birhanu Teshale

    University of Gondar, Ethiopia

    6 publications · 286 citations

    6

    h-index

  5. NP
    Nicole Probst‐Hensch

    University of Basel, Switzerland

    5 publications · 480 citations

    5

    h-index

1–5 of 7.3K

Venues

  1. PLoS ONE

    41 publications · 1.8K citations

    25

    h-index

  2. BMC Public Health

    25 publications · 808 citations

    18

    h-index

  3. Scientific Reports

    11 publications · 315 citations

    12

    h-index

  4. BMJ Open

    11 publications · 325 citations

    11

    h-index

  5. 11

    h-index

1–5