David Chavalarias1, John P A Ioannidis. 1. Centre de Recherche en Epistémologie Appliquée, Ecole Polytechnique-CNRS, 32 Boulevard Victor, Paris, France.
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.
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.
Authors: John P A Ioannidis; Sander Greenland; Mark A Hlatky; Muin J Khoury; Malcolm R Macleod; David Moher; Kenneth F Schulz; Robert Tibshirani Journal: Lancet Date: 2014-01-08 Impact factor: 79.321