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arXiv · 2506.01498

Simulating Complex Crossectional and Longitudinal Data using the simDAG R Package

Robin Denz·Nina Timmesfeld
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Abstract

Generating artificial data is a crucial step when performing Monte-Carlo simulation studies. Depending on the planned study, complex data generation processes (DGP) containing multiple, possibly time-varying, variables with various forms of dependencies and data types may be required. Simulating data from such DGP may therefore become a difficult and time-consuming endeavor. The simDAG R package offers a standardized approach to generate data from simple and complex DGP based on the definition of structural equations in directed acyclic graphs using arbitrary functions or regression models. The package offers a clear syntax with an enhanced formula interface and directly supports generating binary, categorical, count and time-to-event data with arbitrary dependencies, possibly non-linear relationships and interactions. It additionally includes a framework to conduct discrete-time based simulations which allows the generation of longitudinal data on a semi-continuous time-scale. This approach may be used to generate time-to-event data with both recurrent or competing events and possibly multiple time-varying covariates, which may themselves have arbitrary data types. In this article we demonstrate the vast amount of features included in simDAG by replicating the DGP of multiple real Monte-Carlo simulation studies.

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Robin Denz, Nina Timmesfeld. 2025-06-02. Simulating Complex Crossectional and Longitudinal Data using the simDAG R Package. https://doi.org/10.18637/jss.v116.i02

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