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Lookup NU author(s): Dr Anando SenORCiD
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Randomized controlled trials can benefit from proactive assessment of how well their participant selection strategies during the design of eligibility criteria can influence the study generalizability. In this paper, we present a quantitative metric called generalizability index for study traits 2.0 (GIST 2.0) to assess the a priori generalizability (based on population representativeness) of a clinical trial by accounting for the dependencies among multiple eligibility criteria. The metric was evaluated on 16 sepsis trials identified from ClinicalTrials.gov, with their adverse event reports extracted from the trial results sections. The correlation between GIST scores and adverse events was analyzed. We found that the GIST 2.0 score was significantly correlated with total adverse events and serious adverse events (weighted correlation coefficients of 0.825 and 0.709, respectively, with P < 0.01). This study exemplifies the promising use of Big Data in electronic health records and ClinicalTrials.gov for optimizing eligibility criteria design for clinical studies.
Author(s): Sen A, Ryan PB, Goldstein A, Chakrabarti S, Wang S, Koski E, Weng C
Publication type: Article
Publication status: Published
Journal: Annals of the New York Academy of Sciences
Year: 2017
Volume: 1387
Issue: 1
Pages: 34-43
Print publication date: 01/01/2017
Online publication date: 06/09/2016
Acceptance date: 13/07/2016
ISSN (print): 0077-8923
ISSN (electronic): 1749-6632
Publisher: Wiley-Blackwell Publishing, Inc.
URL: https://doi.org/10.1111/nyas.13195
DOI: 10.1111/nyas.13195
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