Sparse Data Bias

Sparse Data Bias is a methodological artifact in meta-analysis arising when limited event counts across studies lead to distorted effect size estimates and inflated Type I error rates, particularly in underpowered cumulative evidence synthesis.

100

Publications

9.6K

Citations

330

Authors

176

Institutions

Publications per year

2017–2026

43

Authors

330

  1. SG
    Sander Greenland

    University of California, Los Angeles, United States

    5 publications · 1.3K citations

    5

    h-index

  2. MA
    Mohammad Alì Mansournia

    Tehran University of Medical Sciences, Iran

    3 publications · 1.2K citations

    3

    h-index

  3. BK
    B. K. Spears

    Lawrence Livermore National Laboratory, United States

    2 publications · 55 citations

    2

    h-index

  4. JJ
    Jayaraman J. Thiagarajan

    Lawrence Livermore National Laboratory, United States

    2 publications · 55 citations

    2

    h-index

  5. HC
    Haitao Chu

    University of Minnesota, United States

    2 publications · 135 citations

    2

    h-index

1–5 of 330

Venues

  1. Statistics in Medicine

    9 publications · 3K citations

    9

    h-index

  2. American Journal of Epidemiology

    AJE · 4 publications · 517 citations

    4

    h-index

  3. International Journal of Epidemiology

    3 publications · 127 citations

    3

    h-index

  4. Technometrics

    2 publications · 79 citations

    2

    h-index

  5. Research Synthesis Methods

    2 publications · 240 citations

    2

    h-index

1–5