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GIST 2.0: A scalable multi-trait metric for quantifying population representativeness of individual clinical studies

Lookup NU author(s): Dr Anando SenORCiD

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Abstract

The design of randomized controlled clinical studies can greatly benefit from iterative assessments of population representativeness of eligibility criteria. We propose a multi-trait metric - GIST 2.0 that can compute the a priori generalizability based on the population representativeness of a clinical study by explicitly modeling the dependencies among all eligibility criteria. We evaluate this metric on twenty clinical studies of two diseases and analyze how a study’s eligibility criteria affect its generalizability (collectively and individually). We statistically analyze the effects of trial setting, trait selection and trait summarizing technique on GIST 2.0. Finally we provide theoretical as well as empirical validations for the expected properties of GIST 2.0.

Publication metadata

Author(s): Sen A, Chakrabarti S, Goldstein A, Wang S, Ryan PB, Weng C

Publication type: Article

Publication status: Published

Journal: Journal of Biomedical Informatics

Year: 2016

Volume: 63

Pages: 325-336

Print publication date: 01/10/2016

Online publication date: 04/09/2016

Acceptance date: 02/09/2016

ISSN (print): 1532-0464

ISSN (electronic): 1532-0480

Publisher: Academic Press

URL: https://doi.org/10.1016/j.jbi.2016.09.003

DOI: 10.1016/j.jbi.2016.09.003

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