Abstract
BackgroundThe best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients.MethodsThe DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package “BoltzmannMachines” from the Julia programming language and wrap it for use with DataSHIELD, which is based on R.ResultsWe present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size.ConclusionsOur implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.
Highlights
The best way to calculate statistics from medical data is to use the data of individual patients
We present the implementation of our approach using deep Boltzmann machines (DBMs) [13] as generative models in DataSHIELD
We show the feasibility of the approach with empirical studies investigating different numbers of sites and sample sizes per site in a distributed analysis, and compare our approach to other types of generative models, namely Generative adversarial network (GAN), Variational autoencoder (VAE), and multivariate imputation by chained equations (MICE)
Summary
The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. One general way to enable such analysis techniques on distributed data is to use a synthetic data approach. Synthetic datasets mimic statistical features of the original data without any linkage to individuals in the original data These synthetic data can be shared across the sites for joint analyses. An approach using bivariate copulas can recreate complex marginal distributions and provide results similar to the original data when using multivariable linear mixed regression for analysis [6]
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