Simulation studies are computer experiments which 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’ 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 article outlines the rationale for using simulation studies and offers guidance for design, execution, analysis, reporting and presentation. In particular, we provide: a structured approach for planning and reporting simulation studies; coherent terminology for simulation studies; guidance on coding simulation studies; a critical discussion of key performance measures and their computation; ideas on structuring tabular and graphical presentation of results; and new graphical presentations. With a view to describing current practice and identifying areas for improvement, we review 100 articles taken from Volume 34 of Statistics in Medicine which included at least one simulation study.