Using simulation studies to evaluate statistical methods

TL;DR

This paper provides a comprehensive guide on designing, analyzing, and reporting simulation studies in statistics, emphasizing best practices and new visualization techniques to improve their quality and interpretability.

Contribution

It introduces a structured framework ('ADEMP') for planning and reporting simulation studies and offers new graphical methods for result presentation.

Findings

Analysis of 100 recent articles reveals common shortcomings in simulation studies.

The proposed ADEMP framework standardizes simulation study design and reporting.

New graphical tools enhance clarity in presenting simulation results.

Abstract

Simulation studies are computer experiments that involve creating data by pseudorandom sampling. The key strength of simulation studies is the ability to understand the behaviour of statistical methods because some 'truth' (usually some parameter/s of interest) is known from the process of generating the data. This allows us to consider properties of methods, such as bias. While widely used, simulation studies are often poorly designed, analysed and reported. This tutorial outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting and presentation. In particular, this tutorial provides: a structured approach for planning and reporting simulation studies, which involves defining aims, data-generating mechanisms, estimands, methods and performance measures ('ADEMP'); coherent terminology for simulation studies; guidance on coding simulation studies; a critical discussion of key performance measures and their estimation; guidance on structuring tabular and graphical presentation of results; and new graphical presentations. With a view to describing recent practice, we review 100 articles taken from Volume 34 of Statistics in Medicine that included at least one simulation study and identify areas for improvement.