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Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials

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, 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 for optimizing eligibility criteria design for clinical studies.

Publication metadata

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.


DOI: 10.1111/nyas.13195


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