ArticleJournal of the American Medical Informatics Association : JAMIA2021
Optimizing the synthesis of clinical trial data using sequential trees.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 44 citations in OpenAlex.
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- 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
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- Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.Journal of medical Internet research · 2025Article
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- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- A synthetic data-driven machine learning approach for athlete performance attenuation prediction.Frontiers in sports and active living · 2025Article
- Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation.JMIR formative research · 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
- SeqTrial: Utility Preserving Sequential Clinical Trial Data Generator.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- 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
- A method for generating synthetic longitudinal health data.BMC medical research methodology · 2023Article
- Validating a membership disclosure metric for synthetic health data.JAMIA open · 2022Article
- Keeping synthetic patients on track: feedback mechanisms to mitigate performance drift in longitudinal health data simulation.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study.JMIR medical informatics · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWith the growing demand for sharing clinical trial data, scalable methods to enable privacy protective access to high-utility data are needed. Data synthesis is one such method. Sequential trees are commonly used to synthesize health data. It is hypothesized that the utility of the generated data is dependent on the variable order. No assessments of the impact of variable order on synthesized clinical trial data have been performed thus far. Through simulation, we aim to evaluate the variability in the utility of synthetic clinical trial data as variable order is randomly shuffled and implement an optimization algorithm to find a good order if variability is too high. MATERIALS AND
methodsSix oncology clinical trial datasets were evaluated in a simulation. Three utility metrics were computed comparing real and synthetic data: univariate similarity, similarity in multivariate prediction accuracy, and a distinguishability metric. Particle swarm was implemented to optimize variable order, and was compared with a curriculum learning approach to ordering variables.
resultsAs the number of variables in a clinical trial dataset increases, there is a pattern of a marked increase in variability of data utility with order. Particle swarm with a distinguishability hinge loss ensured adequate utility across all 6 datasets. The hinge threshold was selected to avoid overfitting which can create a privacy problem. This was superior to curriculum learning in terms of utility.
conclusionsThe optimization approach presented in this study gives a reliable way to synthesize high-utility clinical trial datasets.
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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.