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Using simulation studies to evaluate statistical methods.

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journal contribution
posted on 15.08.2019, 16:11 by Tim P. Morris, Ian R. White, Michael J. Crowther
Simulation studies are computer experiments that involve creating data by pseudo-random sampling. A key strength of simulation studies is the ability to understand the behavior 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, analyzed, 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, which included at least one simulation study and identify areas for improvement.


Tim Morris and Ian White are supported by the Medical Research Council (grant numbers MC_UU_12023/21 and MC_UU_12023/29). Michael Crowther is partly supported by a Medical Research Council New Investigator Research Grant (grant number MR/P015433/1).



Statistics in Medicine, 2019, 38(11), pp. 2074-2102

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