title: Visual aggregate analysis of eligibility features of clinical trials.
abstract: OBJECTIVE
  To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time.
  
  
  METHODS
  Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection.
  
  
  RESULTS
  We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions "hypertension" and "Type 2 diabetes", respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100.
  
  
  CONCLUSIONS
  We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
authors.forename: Zhe
authors.initials: Z
authors.lastname: He
authors.affiliation: Division of General Internal Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.
authors.forename: Simona
authors.initials: S
authors.lastname: Carini
authors.affiliation: Division of General Internal Medicine, University of California, San Francisco, 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. Electronic address: cw2384@cumc.columbia.edu.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D002986:Clinical Trials as Topic / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D020407:Internet; D008297:Male; D008962:Models, Theoretical; D018579:Patient Selection
keywords: Clinical trial; Knowledge management; Patient selection; Selection bias