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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.
Afiliação
  • Azizi Z; Center for Outcomes Research and Evaluation, Faculty of Medicine, McGill University, Montreal, Québec, Canada.
  • Zheng C; Data Science, Replica Analytics Ltd, Ottawa, Ontario, Canada.
  • Mosquera L; Data Science, Replica Analytics Ltd, Ottawa, Ontario, Canada.
  • Pilote L; Medicine, McGill University, Montreal, Québec, Canada.
  • El Emam K; Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Québec, Canada.
BMJ Open ; 11(4): e043497, 2021 04 16.
Article em En | MEDLINE | ID: mdl-33863713
ABSTRACT

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Intervalo Livre de Doença Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Revista: BMJ Open Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Intervalo Livre de Doença Tipo de estudo: Clinical_trials Limite: Humans Idioma: En Revista: BMJ Open Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá