ArticleInternational journal of population data science2022
An overview of synthetic administrative data for research.
Article in International journal of population data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Beyond evidence hierarchies: Leveraging randomized controlled trials and real-world data to advance the value of maternity care.Acta obstetricia et gynecologica Scandinavica · 2026Review
- The Road to Hell Winds On: The High Administrative Burden of Maintaining Linked National Health Data.International journal of population data science · 2026Article
- Leveraging Synthetic Data to Facilitate Research: A Collaborative Model for Analyzing Sensitive National Cancer Registry Data in England.Therapeutic innovation & regulatory science · 2025Article
- Evaluating the utility of data integration with synthetic data and statistical matching.Scientific reports · 2025Article
- Synthetic Data for Sharing and Exploration in High-Performance Sport: Considerations for Application.Sports medicine (Auckland, N.Z.) · 2025Article
- Data Synthesis Reinvented: Preserving Missing Patterns for Enhanced Analysis.IEEE transactions on knowledge and data engineering · 2025Article
- Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.CPT: pharmacometrics & systems pharmacology · 2025Review
- Health Economic Evaluations of Obesity Interventions: Expert Views on How We Can Identify, Interpret, Analyse and Translate Effects.Applied health economics and health policy · 2025Article
- A National Synthetic Populations Dataset for the United States.Scientific data · 2025Article
- A review of synthetic data terminology for privacy preserving use cases.International journal of population data science · 2025Review
- Four checks for low-fidelity synthetic data: recommendations for disclosure control and quality evaluation.International journal of population data science · 2025Article
- HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation.ACM transactions on intelligent systems and technology · 2024Article
- Generating synthetic identifiers to support development and evaluation of data linkage methods.International journal of population data science · 2024Article
- Generating synthetic data from administrative health records for drug safety and effectiveness studies.International journal of population data science · 2023Article
- Federated learning for generating synthetic data: a scoping review.International journal of population data science · 2023Article
- Synthetic data in medical research.BMJ medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which are less likely to hold confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data and describe common methods used to generate synthetic data in the context of UK administrative research. We propose a simplified terminology for categories of synthetic data (univariate, multivariate, and complex modality synthetic data) as well as a more comprehensive description of the terminology used in the existing literature and illustrate challenges and future directions for research.
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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.