ArticleJCO clinical cancer informatics2023
Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets.
Article in JCO clinical cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
16 citing papers in PubMed, 22 citations in OpenAlex.
- Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.Journal of healthcare informatics research · 2026Review
- Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Protecting patient privacy in tabular synthetic health data: a regulatory perspective.NPJ digital medicine · 2025Review
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- The Rethinking Clinical Trials (REaCT) Program: A Pragmatic Research Strategy to Improve Cancer Care for Patients, Caregivers, and Healthcare Systems.Current oncology (Toronto, Ont.) · 2025Review
- Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.Journal of medical Internet research · 2025Article
- Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.CPT: pharmacometrics & systems pharmacology · 2025Review
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- To be or not to be, when synthetic data meet clinical pharmacology: A focused study on pharmacogenetics.CPT: pharmacometrics & systems pharmacology · 2025Article
- Review
- Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.JCO clinical cancer informatics · 2024Article
- An evaluation of synthetic data augmentation for mitigating covariate bias in health data.Patterns (New York, N.Y.) · 2024Article
- An evaluation of the replicability of analyses using synthetic health data.Scientific reports · 2024Article
- The REthinking Clinical Trials Program Retreat 2023: Creating Partnerships to Optimize Quality Cancer Care.Current oncology (Toronto, Ont.) · 2024Article
- Comparative Analysis of Data Generation Techniques for Breast Cancer Research Using Artificial Intelligence.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Big data in breast cancer: Towards precision treatment.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThere is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques.
methodsWe synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk.
resultsUtility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models. DISCUSSION: Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data.
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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.