title: A method for analyzing commonalities in clinical trial target populations.
abstract: ClinicalTrials.gov presents great opportunities for analyzing commonalities in clinical trial target populations to facilitate knowledge reuse when designing eligibility criteria of future trials or to reveal potential systematic biases in selecting population subgroups for clinical research. Towards this goal, this paper presents a novel data resource for enabling such analyses. Our method includes two parts: (1) parsing and indexing eligibility criteria text; and (2) mining common eligibility features and attributes of common numeric features (e.g., A1c). We designed and built a database called "Commonalities in Target Populations of Clinical Trials" (COMPACT), which stores structured eligibility criteria and trial metadata in a readily computable format. We illustrate its use in an example analytic module called CONECT using COMPACT as the backend. Type 2 diabetes is used as an example to analyze commonalities in the target populations of 4,493 clinical trials on this disease.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY.
authors.forename: Zhe
authors.initials: Z
authors.lastname: He
authors.affiliation: Department of Medicine, University of California, San Francisco, San Francisco, CA.
authors.forename: Simona
authors.initials: S
authors.lastname: Carini
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY.
authors.forename: Tianyong
authors.initials: T
authors.lastname: Hao
authors.affiliation: Department of Medicine, University of California, San Francisco, San Francisco, CA.
authors.forename: Ida
authors.initials: I
authors.lastname: Sim
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D002986:Clinical Trials as Topic; D016208:Databases, Factual; D004608:Eligibility Determination / Q000145:classification*; D006801:Humans; D018579:Patient Selection; D012042:Registries