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The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0
OBJECTIVE: The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378875/ https://www.ncbi.nlm.nih.gov/pubmed/29025047 http://dx.doi.org/10.1093/jamia/ocx091 |
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author | Sen, Anando Goldstein, Andrew Chakrabarti, Shreya Shang, Ning Kang, Tian Yaman, Anil Ryan, Patrick B Weng, Chunhua |
author_facet | Sen, Anando Goldstein, Andrew Chakrabarti, Shreya Shang, Ning Kang, Tian Yaman, Anil Ryan, Patrick B Weng, Chunhua |
author_sort | Sen, Anando |
collection | PubMed |
description | OBJECTIVE: The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics. METHODS: Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted. RESULTS: A total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness. CONCLUSIONS: Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials. |
format | Online Article Text |
id | pubmed-7378875 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-73788752020-07-29 The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 Sen, Anando Goldstein, Andrew Chakrabarti, Shreya Shang, Ning Kang, Tian Yaman, Anil Ryan, Patrick B Weng, Chunhua J Am Med Inform Assoc Research and Applications OBJECTIVE: The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics. METHODS: Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted. RESULTS: A total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness. CONCLUSIONS: Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials. Oxford University Press 2017-09-13 /pmc/articles/PMC7378875/ /pubmed/29025047 http://dx.doi.org/10.1093/jamia/ocx091 Text en © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Research and Applications Sen, Anando Goldstein, Andrew Chakrabarti, Shreya Shang, Ning Kang, Tian Yaman, Anil Ryan, Patrick B Weng, Chunhua The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 |
title | The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 |
title_full | The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 |
title_fullStr | The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 |
title_full_unstemmed | The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 |
title_short | The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0 |
title_sort | representativeness of eligible patients in type 2 diabetes trials: a case study using gist 2.0 |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378875/ https://www.ncbi.nlm.nih.gov/pubmed/29025047 http://dx.doi.org/10.1093/jamia/ocx091 |
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