The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 - PubMed (original) (raw)
The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0
Anando Sen et al. J Am Med Inform Assoc. 2018.
Abstract
Objective: The 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.
Methods: Sixty-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.
Results: A 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.
Conclusions: Our 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.
Keywords: clinical trials; eligibility criteria; metadata analysis; population representativeness.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Figures
Figure 1.
Summary of the preprocessing steps: trial selection (top row), trait selection (second row), and definition of target population (third row).
Figure 2.
(A) Histogram of the multitrait representativeness scores (mGIST) for type 2 diabetes trials. (B) The corresponding cumulative distribution function, with the position of the median marked.
Figure 3.
Relative histogram for multitrait representativeness scores (mGIST) of type 2 diabetes trials for different fractions of traits used in the representativeness calculations.
Figure 4.
Relationship between number of eligibility traits and mean representativeness score based on multiple traits (mGIST).
Figure 5.
Relative histogram for multitrait representativeness scores (mGIST) and single-trait representativeness scores for Hba1c (sGIST) for the 1324 trials where Hba1c was an eligibility trait.
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