Optimizing the synthesis of clinical trial data using sequential trees.
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 genera...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 28; no. 1; pp. 3 - 14 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148191059&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148191059 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jan2021 vid: 28 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 148191059 148191059 NLM33186440 148191059 10.1093/jamia/ocaa249 NLM33186440 148191059 ppf: 3 ppct: 11 formats: tig: atl: Optimizing the synthesis of clinical trial data using sequential trees. aug: au: Emam, Khaled El Mosquera, Lucy Zheng, Chaoyi affil: School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa , Ottawa, Ontario, Canada sug: subj: Data Collection Clinical Trials Communication Methods Analysis of Variance Algorithms Human Privacy and Confidentiality Comparative Studies Multicenter Studies Evaluation Research Validation Studies Impact of Events Scale Scales ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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