title: Text mining describes the use of statistical and epidemiological methods in published medical research.
abstract: To describe trends in the use of statistical and epidemiological methods in the medical literature over the past 2 decades.
  
  
  
  We obtained all 1,028,786 articles from the PubMed Central Open-Access archive (retrieved May 9, 2015). We focused on 113,450 medical research articles. A Delphi panel identified 177 statistical/epidemiological methods pertinent to clinical researchers. We used a text-mining approach to determine if a specific statistical/epidemiological method was encountered in a given article. We report the proportion of articles using a specific method for the entire cross-sectional sample and also stratified into three blocks of time (1995-2005; 2006-2010; 2011-2015).
  
  
  
  Numeric descriptive statistics were commonplace (96.4% articles). Other frequently encountered methods groups included statistical inferential concepts (52.9% articles), epidemiological measures of association (53.5% articles) methods for diagnostic/classification accuracy (40.1% articles), hypothesis testing (28.8% articles), ANOVA (23.2% articles), and regression (22.6% articles). We observed relative percent increases in the use of: regression (103.0%), missing data methods (217.9%), survival analysis (147.6%), and correlated data analysis (192.2%).
  
  
  
  This study identified commonly encountered and emergent methods used to investigate medical research problems. Clinical researchers must be aware of the methodological landscape in their field, as statistical/epidemiological methods underpin research claims.
authors.affiliation: Department of Family and Community Medicine, University of Toronto, 500 University Avenue (Suite 346), Toronto, Ontario M5G1V7, Canada. Electronic address: christopher.meaney@utoronto.ca.
authors.forename: Christopher
authors.initials: C
authors.lastname: Meaney
authors.affiliation: Department of Family and Community Medicine, University of Toronto, 500 University Avenue (Suite 346), Toronto, Ontario M5G1V7, Canada.
authors.forename: Rahim
authors.initials: R
authors.lastname: Moineddin
authors.affiliation: Institute of Health Policy, Management and Evaluation, 155 College Street, University of Toronto, Toronto, Ontario M5T3M6, Canada.
authors.forename: Teja
authors.initials: T
authors.lastname: Voruganti
authors.affiliation: Department of Family and Community Medicine, University of Toronto, 500 University Avenue (Suite 346), Toronto, Ontario M5G1V7, Canada.
authors.forename: Mary Ann
authors.initials: MA
authors.lastname: O'Brien
authors.affiliation: Department of Family and Community Medicine, University of Toronto, 500 University Avenue (Suite 346), Toronto, Ontario M5G1V7, Canada.
authors.forename: Paul
authors.initials: P
authors.lastname: Krueger
authors.affiliation: Department of Family and Community Medicine, University of Toronto, 500 University Avenue (Suite 346), Toronto, Ontario M5G1V7, Canada; North York General Hospital, 4001 Leslie St., Toronto, Ontario M2K 1E1, Canada.
authors.forename: Frank
authors.initials: F
authors.lastname: Sullivan
mesh_terms: D035843:Biomedical Research / Q000379:methods* / Q000706:statistics & numerical data*; D001699:Biometry / Q000379:methods*; D003430:Cross-Sectional Studies; D057225:Data Mining; D004812:Epidemiologic Methods; D006801:Humans; D039781:PubMed
keywords: Bibliometrics; Epidemiological methods; Medical research; PubMed; Statistical methods; Text mining