Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies
Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies
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DOI：
10.2196/16492
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发表时间：
2020-02-01
影响因子：
3.2
通讯作者：
Beyar, Rafael
中科院分区：
文献类型：
--
作者：
Benaim, Anat Reiner;Almog, Ronit;Beyar, Rafael
背景：隐私限制限制了出于研究目的获取受保护的患者来源的健康信息。因此，需要进行数据匿名化，以允许研究人员在获得机构审查委员会批准之前访问数据进行初步分析。在我们机构安装并激活的系统可以生成模拟真实的电子病历数据的合成数据，其中只列出虚构的patients.Objective：本文旨在验证分析合成结构化数据时所获得的结果，用于医学研究。一个全面的验证过程中有意义的临床问题和各种类型的数据进行评估的准确性和精确度的统计估计来自合成patient data.Methods：一个跨医院的项目进行了验证结果从合成数据产生的五个当代研究的各种主题。对于每项研究，将合成数据得出的结果与基于真实的数据得出的结果进行比较。此外，重复生成的合成数据集被用来估计从合成data.Results获得的结果的偏差和稳定性：这项研究表明，来自合成数据的结果是预测结果从真实的数据。当患者数量相对于使用的变量数量较大时，在合成数据和真实的数据之间观察到高度准确和高度一致的结果。对于研究的基础上较小的人口占混杂因素和多变量模型的修饰符，预测的准确性适中，但明确的趋势是正确observed.Conclusions：使用合成结构化数据提供了一个接近估计真实的数据结果，因此是一个强大的工具，在塑造研究假设和访问估计分析，而不冒患者隐私。合成数据可以广泛访问数据（例如，组织外的研究人员），并在医院或其他医疗机构中快速，安全和可重复地分析数据，其中患者隐私是主要价值。
Background: Privacy restrictions limit access to protected patient-derived health information for research purposes. Consequently, data anonymization is required to allow researchers data access for initial analysis before granting institutional review board approval. A system installed and activated at our institution enables synthetic data generation that mimics data from real electronic medical records, wherein only fictitious patients are listed.Objective: This paper aimed to validate the results obtained when analyzing synthetic structured data for medical research. A comprehensive validation process concerning meaningful clinical questions and various types of data was conducted to assess the accuracy and precision of statistical estimates derived from synthetic patient data.Methods: A cross-hospital project was conducted to validate results obtained from synthetic data produced for five contemporary studies on various topics. For each study, results derived from synthetic data were compared with those based on real data. In addition, repeatedly generated synthetic datasets were used to estimate the bias and stability of results obtained from synthetic data.Results: This study demonstrated that results derived from synthetic data were predictive of results from real data. When the number of patients was large relative to the number of variables used, highly accurate and strongly consistent results were observed between synthetic and real data. For studies based on smaller populations that accounted for confounders and modifiers by multivariate models, predictions were of moderate accuracy, yet clear trends were correctly observed.Conclusions: The use of synthetic structured data provides a close estimate to real data results and is thus a powerful tool in shaping research hypotheses and accessing estimated analyses, without risking patient privacy. Synthetic data enable broad access to data (eg, for out-of-organization researchers), and rapid, safe, and repeatable analysis of data in hospitals or other health organizations where patient privacy is a primary value.