  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.
