Using simulation studies to evaluate statistical methods
By Tim P. Morris, Ian R. White, Michael J. Crowther. Statistics in Medicine, 2017
1,255 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2149635c8d3a7589260c1960f703b013c3b45fd0
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Citations in the corpus
- Sample Size Justification (2021, neutral): “In these cases simulations can provide a flexible solution to perform power analyses for any test (Morris et al., 2019).”
- Handling Skewed Data: A Comparison of Two Popular Methods (2020, background): “An essential asset of imulatio studies is the capability to understand and study the performance of statistical methods because parameters of di ributions are known in advance from the p ocess of generating the ata [3].”
- The consequences of checking for zero‐inflation and overdispersion in the analysis of count data (2019, methodology): “while also reducing the amount of Monte Carlo standard error to a negligible amount (for looking at type 1 error with = 0:05, Monte Carlo SE will be approximately 0:0018 ˇ p 0:05(1 0:05)=15;000); see Morris et al. (2019). To test the association between X and Y with each of the regression models, we”