# Statistical Plasmode Simulations -- Potentials, Challenges and Recommendations

arXiv: https://arxiv.org/abs/2305.06028 · HTML: https://mlsanity.com/paper/2305.06028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2023.

## Built on by (most cited first, 2 of 2)

- [High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates](https://mlsanity.com/paper/2405.10925/) (2024)
- [Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning](https://mlsanity.com/paper/2512.13610/) (2025)

## Abstract (arXiv)

Statistical data simulation is essential in the development of statistical models and methods as well as in their performance evaluation. To capture complex data structures, in particular for high-dimensional data, a variety of simulation approaches have been introduced including parametric and the so-called plasmode simulations. While there are concerns about the realism of parametrically simulated data, it is widely claimed that plasmodes come very close to reality with some aspects of the "truth'' known. However, there are no explicit guidelines or state-of-the-art on how to perform plasmode data simulations. In the present paper, we first review existing literature and introduce the concept of statistical plasmode simulation. We then discuss advantages and challenges of statistical plasmodes and provide a step-wise procedure for their generation, including key steps to their implementation and reporting. Finally, we illustrate the concept of statistical plasmodes as well as the proposed plasmode generation procedure by means of a public real RNA dataset on breast carcinoma patients.