title: A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records.
abstract: OBJECTIVE
  To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.
  
  
  METHODS
  Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.
  
  
  RESULTS
  Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75.
  
  
  CONCLUSIONS
  We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generalizability of clinical trials and for defining population-representative clinical trial eligibility criteria.
authors.affiliation: Department of Biomedical Informatics, Columbia University , New York, NY 10032.
authors.forename: C
authors.initials: C
authors.lastname: Weng
authors.affiliation: Department of Computer Science, City College of New York , New York, NY 10031.
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.affiliation: Janssen Research and Development , Titusville, New Jersey, 08560 ; Observational Health Data Sciences and Informatics , New York, NY, 10032.
authors.forename: P
authors.initials: P
authors.lastname: Ryan
authors.affiliation: Department of Biostatistics, Columbia University , New York, NY 10032.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Informatics, Columbia University , New York, NY 10032.
authors.forename: F
authors.initials: F
authors.lastname: Liu
authors.affiliation: Business School, Columbia University , New York, NY 10025.
authors.forename: J
authors.initials: J
authors.lastname: Gao
authors.affiliation: Department of Medicine, Columbia University , New York, NY 10032.
authors.forename: J T
authors.initials: JT
authors.lastname: Bigger
authors.affiliation: Department of Biomedical Informatics, Columbia University , New York, NY 10032.
authors.forename: G
authors.initials: G
authors.lastname: Hripcsak
mesh_terms: D000046:Academic Medical Centers / Q000706:statistics & numerical data; D017677:Age Distribution; D002986:Clinical Trials as Topic / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis; D057286:Electronic Health Records; D004608:Eligibility Determination; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D016247:Information Storage and Retrieval; D007297:Inpatients / Q000706:statistics & numerical data; D020407:Internet; D008297:Male; D008875:Middle Aged; D010045:Outpatients / Q000706:statistics & numerical data; D018579:Patient Selection
chemical_list: D006442:Glycated Hemoglobin
keywords: Clinical trials; clinical research informatics; comparative effectiveness research; electronic health records; meta-analysis; selection bias