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Lookup NU author(s): Dr Anando SenORCiD
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ObjectiveThe population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics.MethodsSixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted.ResultsA total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness.ConclusionsOur study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials.
Author(s): Sen A, Goldstein A, Chakrabarti S, Shang S, Kang T, Yaman A, Ryan PB, Weng C
Publication type: Article
Publication status: Published
Journal: Journal of the American Medical Informatics Association
Year: 2018
Volume: 25
Issue: 3
Pages: 239-247
Print publication date: 01/03/2018
Online publication date: 13/09/2017
Acceptance date: 08/08/2017
ISSN (electronic): 1527-974X
Publisher: Oxford University Press
URL: https://doi.org/10.1093/jamia/ocx091
DOI: 10.1093/jamia/ocx091
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