ArticleStatistics in medicine2013
Disclosure control using partially synthetic data for large-scale health surveys, with applications to CanCORS.
Article in Statistics in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- 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
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- An evaluation of the replicability of analyses using synthetic health data.Scientific reports · 2024Article
- Privacy-preserving analysis of time-to-event data under nested case-control sampling.Statistical methods in medical research · 2024Article
- Harnessing the power of synthetic data in healthcare: innovation, application, and privacy.NPJ digital medicine · 2023Review
- Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets.JCO clinical cancer informatics · 2023Article
- A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health.Scientific reports · 2023Article
- Synthetic data in health care: A narrative review.PLOS digital health · 2023Article
- Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation.Journal of medical Internet research · 2020Article
- Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets.IEEE transactions on knowledge and data engineering · 2018Article
- Confidence interval estimation in R-DAS.Drug and alcohol dependence · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Statistical agencies have begun to partially synthesize public-use data for major surveys to protect the confidentiality of respondents' identities and sensitive attributes by replacing high disclosure risk and sensitive variables with multiple imputations. To date, there are few applications of synthetic data techniques to large-scale healthcare survey data. Here, we describe partial synthesis of survey data collected by the Cancer Care Outcomes Research and Surveillance (CanCORS) project, a comprehensive observational study of the experiences, treatments, and outcomes of patients with lung or colorectal cancer in the USA. We review inferential methods for partially synthetic data and discuss selection of high disclosure risk variables for synthesis, specification of imputation models, and identification disclosure risk assessment. We evaluate data utility by replicating published analyses and comparing results using original and synthetic data and discuss practical issues in preserving inferential conclusions. We found that important subgroup relationships must be included in the synthetic data imputation model, to preserve the data utility of the observed data for a given analysis procedure. We conclude that synthetic CanCORS data are suited best for preliminary data analyses purposes. These methods address the requirement to share data in clinical research without compromising confidentiality.
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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.