ArticleMethods of information in medicine2023
A Simple-to-Use R Package for Mimicking Study Data by Simulations.
Article in Methods of information in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Calibrating Machine Learning Approaches for Probability Estimation Without Calibration Data.Statistics in medicine · 2026Article
- Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups.BMC medical research methodology · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundData protection policies might prohibit the transfer of existing study data to interested research groups. To overcome legal restrictions, simulated data can be transferred that mimic the structure but are different from the existing study data.
objectivesThe aim of this work is to introduce the simple-to-use R package Mock Data Generation (modgo) that may be used for simulating data from existing study data for continuous, ordinal categorical, and dichotomous variables.
methodsThe core is to combine rank inverse normal transformation with the calculation of a correlation matrix for all variables. Data can then be simulated from a multivariate normal and transferred back to the original scale of the variables. Unique features of modgo are that it allows to change the correlation between variables, to perform perturbation analysis, to handle multicenter data, and to change inclusion/exclusion criteria by selecting specific values of one or a set of variables. Simulation studies on real data demonstrate the validity and flexibility of modgo.
resultsmodgo mimicked the structure of the original study data. Results of modgo were similar with those from two other existing packages in standard simulation scenarios. modgo's flexibility was demonstrated on several expansions.
conclusionThe R package modgo is useful when existing study data may not be shared. Its perturbation expansion permits to simulate truly anonymized subjects. The expansion to multicenter studies can be used for validating prediction models. Additional expansions can support the unraveling of associations even in large study data and can be useful in power calculations.
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