Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials. Issue 1 (6th September 2016)

• Record Type: Journal Article
• Authors: Sen, Anando; Ryan, Patrick B.; Goldstein, Andrew; Chakrabarti, Shreya; Wang, Shuang; Koski, Eileen; Weng, Chunhua
• Is Part Of: Annals of the New York Academy of Sciences. Volume 1387:Issue 1(2017)
• Page Start: 34
• Page End: 43
• Publication Date: 2016-09-06
• Subjects: clinical trials -- generalizability -- population representativeness -- eligibility criteria -- trait dependencies -- adverse events; Medical sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; 610
• DOI: 10.1111/nyas.13195
• Languages: English
• ISSNs: 0077-8923

Abstract

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.