ArticleJournal of biomedical informatics2015
Visual aggregate analysis of eligibility features of clinical trials.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety.Journal of biomedical informatics · 2022Article
- A knowledge base of clinical trial eligibility criteria.Journal of biomedical informatics · 2021Article
- Patient Engagement in Medical Research Among Older Adults: Analysis of the Health Information National Trends Survey.Journal of medical Internet research · 2019Article
- EliIE: An open-source information extraction system for clinical trial eligibility criteria.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Clinical Research Informatics: Supporting the Research Study Lifecycle.Yearbook of medical informatics · 2017Review
- Comparing and Contrasting A Priori and A Posteriori Generalizability Assessment of Clinical Trials on Type 2 Diabetes Mellitus.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Clinical Research Informatics for Big Data and Precision Medicine.Yearbook of medical informatics · 2016Article
- Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries.BMC bioinformatics · 2016Article
- Assessing the population representativeness of colorectal cancer treatment clinical trials.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2016Article
- Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text.Methods of information in medicine · 2016Article
- Multivariate analysis of the population representativeness of related clinical studies.Journal of biomedical informatics · 2016Article
- Optimizing Clinical Research Participant Selection with Informatics.Trends in pharmacological sciences · 2015Article
- Simulation-based Evaluation of the Generalizability Index for Study Traits.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveTo 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.
methodsUsing 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.
resultsWe 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.
conclusionsWe 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.