ObjectivesThere 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.SettingReplication of a published stage III colon cancer trial secondary analysis using synthetic data generated by a machine learning method.ParticipantsThere were 1543 patients in the control arm that were included in our analysis.Primary and secondary outcome measuresAnalyses 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.ResultsAnalysis 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).ConclusionsThe 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 numberNCT00079274.
Objective With 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 Methods Six 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. Results As 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. Conclusions The optimization approach presented in this study gives a reliable way to synthesize high-utility clinical trial datasets.
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