title: Multivariate analysis of the population representativeness of related clinical studies.
abstract: OBJECTIVE
  To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.
  
  
  METHODS
  We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.
  
  
  RESULTS
  According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.
  
  
  CONCLUSIONS
  mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Electronic address: zh2132@columbia.edu.
authors.forename: Zhe
authors.initials: Z
authors.lastname: He
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; Janssen Research and Development, Titusville, NJ 08560, USA; Observational Health Data Sciences and Informatics, New York, NY 10032, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Ryan
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
authors.forename: Julia
authors.initials: J
authors.lastname: Hoxha
authors.affiliation: Department of Biostatistics, Columbia University, New York, NY 10032, USA.
authors.forename: Shuang
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Medicine, University of California, San Francisco, CA 94143, USA.
authors.forename: Simona
authors.initials: S
authors.lastname: Carini
authors.affiliation: Department of Medicine, University of California, San Francisco, CA 94143, USA.
authors.forename: Ida
authors.initials: I
authors.lastname: Sim
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; Observational Health Data Sciences and Informatics, New York, NY 10032, USA.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000592:standards*; D002986:Clinical Trials as Topic; D016208:Databases, Factual; D003710:Demography / Q000379:methods*; D003924:Diabetes Mellitus, Type 2; D006801:Humans; D008492:Medical Informatics Computing; D015999:Multivariate Analysis; D009749:Nutrition Surveys; D064887:Observational Studies as Topic; D018579:Patient Selection; D015983:Selection Bias
keywords: Clinical trial; Knowledge representation; Selection bias
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