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Bias01:22

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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Authorship network bias in meta-analysis.

Marvin Rieck1, Anne-Christine Mupepele2, Carsten F Dormann1

  • 1Department of Biometry and Environmental System Analysis, https://ror.org/0245cg223University of Freiburg, Germany.

Research Synthesis Methods
|February 18, 2026
PubMed
Summary
This summary is machine-generated.

Authorship network bias can skew meta-analysis results. This study introduces a new method to detect and correct this bias, enhancing the reliability of quantitative research synthesis.

Keywords:
author dependenceauthorship influenceauthorship network biascollaboration networkmeta-analysisnon-independence

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Area of Science:

  • Statistics
  • Biostatistics
  • Bibliometrics

Background:

  • Meta-analyses are crucial for quantitative research synthesis but susceptible to biases.
  • Authorship network bias, stemming from overlapping authors in primary studies, can compromise meta-analysis quality.
  • This bias arises from the non-independence of effect sizes due to shared authorship.

Purpose of the Study:

  • To introduce a novel method for detecting and correcting authorship network bias in meta-analyses.
  • To enhance the reliability and validity of quantitative research synthesis.
  • To address an often-overlooked source of bias in meta-analytic studies.

Main Methods:

  • Proposed a new method leveraging authorship networks to identify non-independent effect sizes.
  • Utilized multilevel models with author clusters as a hierarchical level for bias accounting.
  • Validated the method through analysis of simulated data and nine exemplary meta-analyses.

Main Results:

  • The new method effectively detects and corrects non-independent effect sizes caused by authorship overlap.
  • Simulated data analysis confirmed the method's effectiveness.
  • Application to real-world meta-analyses demonstrated its practical utility.

Conclusions:

  • The developed method offers a reliable approach to mitigate authorship network bias in meta-analyses.
  • This technique can be readily integrated into existing meta-analysis workflows, particularly using R's metafor package.
  • Addressing authorship network bias is essential for improving the overall quality and trustworthiness of meta-analytic findings.