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Science mapping analysis characterizes 235 biases in biomedical research

  • David Chavalarias
    Affiliations
    Centre de Recherche en Épistémologie Appliquée, École Polytechnique-CNRS, 32 Bd Victor, 75015 Paris, France

    Institut des Systèmes Complexes de Paris Ile-de-France, 57–59 rue Lhomond, 75005, Paris, France
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  • John P.A. Ioannidis
    Correspondence
    Corresponding author. Tel: + 30-265-1007807; fax: + 30-265-1007867.
    Affiliations
    Department of Hygiene and Epidemiology, University of Ioannina School of Medicine and Biomedical Research Institute, Foundation for Research and Technology-Hellas, Ioannina 45110, Greece

    Tufts Clinical and Translational Science Institute and Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Department of Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
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      Abstract

      Objective

      Many different types of bias have been described. Some biases may tend to coexist or be associated with specific research settings, fields, and types of studies. We aimed to map systematically the terminology of bias across biomedical research.

      Study Design and Setting

      We used advanced text-mining and clustering techniques to evaluate 17,265,924 items from PubMed (1958–2008). We considered 235 bias terms and 103 other terms that appear commonly in articles dealing with bias.

      Results

      Forty bias terms were used in the title or abstract of more than 100 articles each. Pseudo-inclusion clustering identified 252 clusters of terms. The clusters were organized into macroscopic maps that cover a continuum of research fields. The resulting maps highlight which types of biases tend to co-occur and may need to be considered together and what biases are commonly encountered and discussed in specific fields. Most of the common bias terms have had continuous use over time since their introduction, and some (in particular confounding, selection bias, response bias, and publication bias) show increased usage through time.

      Conclusion

      This systematic mapping offers a dynamic classification of biases in biomedical investigation and related fields and can offer insights for the multifaceted aspects of bias.

      Keywords

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