Volume 1387, Issue 1 p. 34-43
Original Article

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

Anando Sen

Anando Sen

Department of Biomedical Informatics, Columbia University, New York, New York

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Patrick B. Ryan

Patrick B. Ryan

Department of Biomedical Informatics, Columbia University, New York, New York

Janssen Research and Development, Titusville, New Jersey

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Andrew Goldstein

Andrew Goldstein

Department of Biomedical Informatics, Columbia University, New York, New York

Department of Medicine, New York University, New York, New York

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Shreya Chakrabarti

Shreya Chakrabarti

Department of Biomedical Informatics, Columbia University, New York, New York

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Shuang Wang

Shuang Wang

Department of Biostatistics, Columbia University, New York, New York

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Eileen Koski

Eileen Koski

Center for Computational Health, IBM T.J. Watson Research Center, Yorktown Heights, New York

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Chunhua Weng

Corresponding Author

Chunhua Weng

Department of Biomedical Informatics, Columbia University, New York, New York

Address for correspondence: Chunhua Weng, Ph.D., Department of Biomedical Informatics, Columbia University, 622 W. 168th St., PH-20-407, New York, NY 10032. [email protected]Search for more papers by this author
First published: 06 September 2016
Citations: 15

Abstract

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.