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

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.

The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0

“…The previously published GIST 2.0 methodology 16 computes a multiple-trait GIST (mGIST) score for the entire study and one single-trait GIST (sGIST) score for each eligibility trait. The mGIST score (when computed using all traits) approximates the fraction of the target population that would be eligible for the study.…”

“…We previously designed the Generalizability Index of Study Traits (GIST) metric to measure the a priori representativeness of eligibility criteria of related trials, 6 and extended GIST to GIST 2.0. 16 The GIST 2.0 methodology differs from GIST and other representativeness metrics, such as propensity scores, 17 , 18 due to its explicit modeling of trait dependencies. We previously used GIST to compute the collective population representativeness of multiple related type 2 diabetes clinical studies using multiple study traits.…”

Finding Success in Clinical Trial Recruitment: A Trial Registry Analysis

“…This implies that relaxation of eligibility restrictiveness can be pursued without exposing patients to unnecessary risk. In fact, a recent analysis tool, titled GIST, was developed just for this purpose (37,43). Further, improved access to clinical site information should be leveraged to carefully choose where new sites are to open, avoiding direct competition with nearby trials and allowing patients in more areas to have access to these options.…”

An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model Version 5.0

“…For this, we need to measure the representativeness of populations enrolled in clinical studies. This will be an extension of our work on quantifying clinical study eligibility based representativeness [51]. …”