The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Using simulation studies to evaluate statistical methods
Open accessAbstract
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.
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Cites
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“The sample estimate V̂ar(̂) should be a consistent estimate of the sampling variance of ̂ (see for example (18)).”
BackgroundMultiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
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Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
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Imputing missing covariate values for the Cox model
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Self-citationComputer Age Statistical Inference: Algorithms, Evidence, and Data Science
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Multiple-Imputation Inferences with Uncongenial Sources of Input
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Cited by
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229
Daniel, R., Zhang, J., & Farewell, D. (2021) · Biometrical Journal · doi:10.1002/bimj.201900297 · Cited by 167
Multiple imputation with missing data indicators.
Beesley, L. J., Bondarenko, I., Elliot, M. R., Kurian, A. W., Katz, S. J., & Taylor, J. M. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211047346 · Cited by 114
Sisk, R., Sperrin, M., Peek, N., van Smeden, M., & Martin, G. P. (2023) · Statistical Methods in Medical Research · doi:10.1177/09622802231165001 · Cited by 99
Ju, K., Lin, L., Chu, H., Cheng, L.-L., & Xu, C. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01035-6 · Cited by 61
Metadata
- Authors
- Morris, T. P., White, I. R., & Crowther, M. J.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2019-01-16
- Volume
- 38
- Issue
- 11
- Pages
- 2074-2102
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2775363893
- Cited by
- 1,305
- References
- 58
- FWCI
- 88.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
