DOI: 10.1101/19001305Dec 16, 2019Paper

Using collaboration networks to identify authorship bias in meta-analyses

MedRxiv : the Preprint Server for Health Sciences
Thiago C Moulin, O. B. Amaral


Meta-analytic methods are powerful resources to summarize the existing evidence concerning a given research question, and are widely used in many academic fields. However, meta-analyses can be vulnerable to various sources of bias, which should be considered to avoid inaccuracies. Many of these sources can be related to study authorship, as both methodological choices and researcher bias may lead to deviations in results between different research groups. In this work, we describe a method to objectively attribute study authorship within a given meta-analysis to different research groups by using graph cluster analysis of collaboration networks. We then provide empirical examples of how the research group of origin can impact effect size in distinct types of meta-analyses, demonstrating how non-independence between within-group results can bias effect size estimates if uncorrected. Finally, we show that multilevel random-effects models using research group as a level of analysis can be a simple tool for correcting biases related to study authorship.

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