Speaker
Description
Sharing of original study data may be restricted by data protection policies. Instead, synthetic data that mimics the original data structure may be shared between research groups. This work introduces modgo 2.0 which may be used for generating synthetic data from existing study data. Simulations may be based either on the combination of the rank inverse normal transformation with simulation from the multivariate normal or on the use of the generalized lambda and/or generalized Poisson distributions. Scales of the variables may be continuous, ordinal, categorical, dichotomous, and/or even survival. We also provide an extension to the simulation of survey data which contains weights. Simulations on real data demonstrate the flexible use of modgo. The R package modgo is useful when existing study data may not be shared. Unique features are the inclusion of survival data and the expansion to simulating survey data. Its novel expansion to the generalized lambda and Poisson distributions permits the sharing of truly anonymized subjects.
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