Science mapping analysis characterizes 235 biases

in biomedical research

Objective: Many different types of bias have been described.  Some biases may tend to co-exist 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: Fourty bias terms were used in the title or abstract of over 100 articles each. Paradigmatic proximity 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 a 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.