GIST 2.0: A scalable multi-trait metric for quantifying population representativeness of individual clinical studies.
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Where this comes from
- Record sourced from PubMed, PMID 27600407.
- Also identified by DOI 10.1016/j.jbi.2016.09.003 and PMC identifier 5077682.
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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.
Medical subject headings
- Patient Selection
- Randomized Controlled Trials as Topic