Visual aggregate analysis of eligibility features of clinical trials

Abstract

Objective To develop a method for profiling the collective populations targeted for recruitment by multiple clinical studies addressing the same medical condition using one eligibility feature each time. Methods Using a previously published database COMPACT as the backend, we designed a scalable method for visual aggregate analysis of clinical trial eligibility features. This method consists of four modules for eligibility feature frequency analysis, query builder, distribution analysis, and visualization, respectively. This method is capable of analyzing (1) frequently used qualitative and quantitative features for recruiting subjects for a selected medical condition, (2) distribution of study enrollment on consecutive value points or value intervals of each quantitative feature, and (3) distribution of studies on the boundary values, permissible value ranges, and value range widths of each feature. All analysis results were visualized using Google Charts API. Five recruited potential users assessed the usefulness of this method for identifying common patterns in any selected eligibility feature for clinical trial participant selection. Results We implemented this method as a Web-based analytical system called VITTA (Visual Analysis Tool of Clinical Study Target Populations). We illustrated the functionality of VITTA using two sample queries involving quantitative features BMI and HbA1c for conditions “hypertension” and “Type 2 diabetes”, respectively. The recruited potential users rated the user-perceived usefulness of VITTA with an average score of 86.4/100. Conclusions We contributed a novel aggregate analysis method to enable the interrogation of common patterns in quantitative eligibility criteria and the collective target populations of multiple related clinical studies. A larger-scale study is warranted to formally assess the usefulness of VITTA among clinical investigators and sponsors in various therapeutic areas.

Multivariate analysis of the population representativeness of related clinical studies

“…We have created a web-based visualization tool VITTA (http://is.gd/VITTA) to show how studies vary in their study populations with respect to study traits, one at a time [20]. Different trait names and measurement units were normalized by the Valx system [24] developed in house.…”

“…To match the year range for patient data retrieved from NHANES (described in Section 2.1.2), we included 3,158 T2DM studies with a start date between January 2003 and December 2012 for analysis in this study. We used the VITTA system [20] to find frequent quantitative variables in the eligible criteria of all the selected 3,158 T2DM studies. Age, HbA1c and BMI were identified as the most frequently used quantitative variables, appearing in 97.1% (3,066), 48.6% (1,534), and 42.8% (1,351) of the selected T2DM studies, respectively.…”

Assessing the population representativeness of colorectal cancer treatment clinical trials

“…Based on the COMPACT database, we have developed a web-based tool VITTA (http://is.gd/VITTA), allowing its users to flexibly select a set of trials of the same disease domain and profile their study population with respect to one quantitative eligibility criteria at a time [16]. Using the VITTA tool, we identified 1,308 CRC treatment trials with a start date between 01/2004 and 12/2013.…”

Clinical Research Informatics: Supporting the Research Study Lifecycle

“…One group of these articles provided support for designing various aspects of study design, such as managing confounding factors, comparing placebos, stratification, adaptive designs, group designs, and so-called n of one studies [24][25][26][27][28][29]. Another group examined broader aspects of protocol design, such as assessing the feasibility of the study, preparing the protocol for the institutional review board (IRB), reducing fraudulent behavior in internet-based studies, managing the protocol across multiple sites, and managing protocols of multiple studies [30][31][32][33][34].…”