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Can synthetic data be a proxy for real clinical trial data? A validation study
OBJECTIVES: There are increasing requirements to make research data, especially clinical trial data, more broadly available for secondary analyses. However, data availability remains a challenge due to complex privacy requirements. This challenge can potentially be addressed using synthetic data. SE...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055130/ https://www.ncbi.nlm.nih.gov/pubmed/33863713 http://dx.doi.org/10.1136/bmjopen-2020-043497 |
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author | Azizi, Zahra Zheng, Chaoyi Mosquera, Lucy Pilote, Louise El Emam, Khaled |
author_facet | Azizi, Zahra Zheng, Chaoyi Mosquera, Lucy Pilote, Louise El Emam, Khaled |
author_sort | Azizi, Zahra |
collection | PubMed |
description | OBJECTIVES: There are increasing requirements to make research data, especially clinical trial data, more broadly available for secondary analyses. However, data availability remains a challenge due to complex privacy requirements. This challenge can potentially be addressed using synthetic data. SETTING: Replication of a published stage III colon cancer trial secondary analysis using synthetic data generated by a machine learning method. PARTICIPANTS: There were 1543 patients in the control arm that were included in our analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Analyses from a study published on the real dataset were replicated on synthetic data to investigate the relationship between bowel obstruction and event-free survival. Information theoretic metrics were used to compare the univariate distributions between real and synthetic data. Percentage CI overlap was used to assess the similarity in the size of the bivariate relationships, and similarly for the multivariate Cox models derived from the two datasets. RESULTS: Analysis results were similar between the real and synthetic datasets. The univariate distributions were within 1% of difference on an information theoretic metric. All of the bivariate relationships had CI overlap on the tau statistic above 50%. The main conclusion from the published study, that lack of bowel obstruction has a strong impact on survival, was replicated directionally and the HR CI overlap between the real and synthetic data was 61% for overall survival (real data: HR 1.56, 95% CI 1.11 to 2.2; synthetic data: HR 2.03, 95% CI 1.44 to 2.87) and 86% for disease-free survival (real data: HR 1.51, 95% CI 1.18 to 1.95; synthetic data: HR 1.63, 95% CI 1.26 to 2.1). CONCLUSIONS: The high concordance between the analytical results and conclusions from synthetic and real data suggests that synthetic data can be used as a reasonable proxy for real clinical trial datasets. TRIAL REGISTRATION NUMBER: NCT00079274. |
format | Online Article Text |
id | pubmed-8055130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-80551302021-04-28 Can synthetic data be a proxy for real clinical trial data? A validation study Azizi, Zahra Zheng, Chaoyi Mosquera, Lucy Pilote, Louise El Emam, Khaled BMJ Open Health Informatics OBJECTIVES: There are increasing requirements to make research data, especially clinical trial data, more broadly available for secondary analyses. However, data availability remains a challenge due to complex privacy requirements. This challenge can potentially be addressed using synthetic data. SETTING: Replication of a published stage III colon cancer trial secondary analysis using synthetic data generated by a machine learning method. PARTICIPANTS: There were 1543 patients in the control arm that were included in our analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Analyses from a study published on the real dataset were replicated on synthetic data to investigate the relationship between bowel obstruction and event-free survival. Information theoretic metrics were used to compare the univariate distributions between real and synthetic data. Percentage CI overlap was used to assess the similarity in the size of the bivariate relationships, and similarly for the multivariate Cox models derived from the two datasets. RESULTS: Analysis results were similar between the real and synthetic datasets. The univariate distributions were within 1% of difference on an information theoretic metric. All of the bivariate relationships had CI overlap on the tau statistic above 50%. The main conclusion from the published study, that lack of bowel obstruction has a strong impact on survival, was replicated directionally and the HR CI overlap between the real and synthetic data was 61% for overall survival (real data: HR 1.56, 95% CI 1.11 to 2.2; synthetic data: HR 2.03, 95% CI 1.44 to 2.87) and 86% for disease-free survival (real data: HR 1.51, 95% CI 1.18 to 1.95; synthetic data: HR 1.63, 95% CI 1.26 to 2.1). CONCLUSIONS: The high concordance between the analytical results and conclusions from synthetic and real data suggests that synthetic data can be used as a reasonable proxy for real clinical trial datasets. TRIAL REGISTRATION NUMBER: NCT00079274. BMJ Publishing Group 2021-04-16 /pmc/articles/PMC8055130/ /pubmed/33863713 http://dx.doi.org/10.1136/bmjopen-2020-043497 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Health Informatics Azizi, Zahra Zheng, Chaoyi Mosquera, Lucy Pilote, Louise El Emam, Khaled Can synthetic data be a proxy for real clinical trial data? A validation study |
title | Can synthetic data be a proxy for real clinical trial data? A validation study |
title_full | Can synthetic data be a proxy for real clinical trial data? A validation study |
title_fullStr | Can synthetic data be a proxy for real clinical trial data? A validation study |
title_full_unstemmed | Can synthetic data be a proxy for real clinical trial data? A validation study |
title_short | Can synthetic data be a proxy for real clinical trial data? A validation study |
title_sort | can synthetic data be a proxy for real clinical trial data? a validation study |
topic | Health Informatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055130/ https://www.ncbi.nlm.nih.gov/pubmed/33863713 http://dx.doi.org/10.1136/bmjopen-2020-043497 |
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