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A Simple-to-Use R Package for Mimicking Study Data by Simulations.

Giorgos Koliopanos1, Francisco Ojeda2,3, Andreas Ziegler1,2,3,4

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The R package modgo generates simulated study data when data sharing is restricted. This tool mimics original data structure, ensuring privacy and enabling further research, including multicenter studies and power calculations.

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Area of Science:

  • Biostatistics
  • Data Science
  • Health Informatics

Background:

  • Data protection policies often restrict sharing of sensitive study data.
  • Simulated data offers a legal alternative, preserving data structure while ensuring privacy.

Purpose of the Study:

  • Introduce the R package Mock Data Generation (modgo) for simulating study data.
  • Provide a user-friendly tool for generating continuous, ordinal, and dichotomous variables.

Main Methods:

  • Utilizes rank inverse normal transformation and correlation matrix calculation.
  • Simulates data from a multivariate normal distribution, transforming back to original variable scales.
  • Offers features for altering variable correlations, perturbation analysis, and handling multicenter data.

Main Results:

  • modgo successfully mimicked the structure of original study data.
  • Simulation results were comparable to existing data simulation packages.
  • Demonstrated flexibility through various expansion scenarios and real-data validation.

Conclusions:

  • modgo is valuable for research involving restricted data sharing, offering anonymized subject simulation.
  • Supports multicenter study validation for prediction models and unraveling associations in large datasets.
  • Facilitates power calculations and enhances data analysis capabilities.