Multivariate analysis of the population representativeness of related clinical studies

Abstract

Objective To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies. Methods We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called “Multivariate Underrepresented Subgroup Identification” (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data. Results According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65 years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment. Conclusions mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables

“…He et al later validated the GIST approach using clinical trial data and NHANES data and concluded patients enrolled in type II diabetes trials are younger, with lower body mass index (BMI) and higher HbA1c than the general patient population (He et al, 2015). mGIST (ClinicalTrials.gov + NHANES data) (He et al, 2016) Patient representative analysis of clinical trials using public survey datasets (NHANES); multivariate model; more effective and efficient in comparing representativeness of multiple study sets; NHANES data not limited to admitted patients and is well-structured and readily analyzed Lack of longitudinal analysis and use of self-reported medical conditions (NHANES data); does not assess clinical relevance of factors (each variable weighted equally); data quality issues with ClinicalTrials.gov (potential for missing data)…”

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

“…The TP subsumes all other populations. It is impossible to characterize this population exactly, but it can often be approximated by the EP [14]. The dashed outline around the TP in Figure 1 indicates that the TP is not exactly defined.…”

“…Though initially presented only for individual traits, GIST is capable of computing generalizability with multiple traits [14]. However, as with the other studies mentioned above, GIST did not account for the dependencies and significance of traits.…”

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

“…Weng et al introduced the generalizability index for study traits (GIST) as a figure of merit for assessing generalizability. 17 In its most general form, GIST could compute the generalizability of multiple-related trials (of the same disease) at the same time and was initially presented for individual study traits, but was later extended to multiple traits by He et al 33 However, GIST had a few limitations. First, all clinical traits were considered independently and the dependencies between them were not accounted for.…”