The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The design of simulation studies in medical statistics
Abstract
Simulation studies use computer intensive procedures to assess the performance of a variety of statistical methods in relation to a known truth. Such evaluation cannot be achieved with studies of real data alone. Designing high‐quality simulations that reflect the complex situations seen in practice, such as in prognostic factors studies, is not a simple process. Unfortunately, very few published simulation studies provide sufficient details to allow readers to understand fully all the processes required to design a simulation study. When planning a simulation study, it is recommended that a detailed protocol be produced, giving full details of how the study will be performed, analysed and reported. This paper details the important considerations necessary when designing any simulation study, including defining specific objectives of the study, determining the procedures for generating the data sets and the number of simulations to perform. A checklist highlighting the important considerations when designing a simulation study is provided. A small review of the literature identifies the current practices within published simulation studies. Copyright © 2006 John Wiley & Sons, Ltd.
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Cites
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
On the practice of dichotomization of quantitative variables.
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.19 · Cited by 3,538 · cited 2×
“generated as continuous variables and then dichotomized, but the covariate correlation structure used to generate the continuous variable needs to be adjusted to provide the correct correlation with the resulting binary variable [16].”
BackgroundMethodSimulation and the Monte Carlo Method
Rubinstein, R. Y., & Kroese, D. P. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118631980 · Cited by 3,356 · cited 1×
“SE(̂)/ √ B, but this can be computational expensive and therefore variance reduction techniques could be employed [24].”
MethodA comparison of inclusive and restrictive strategies in modern missing data procedures.
Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.330 · Cited by 2,389
Controlling the risk of spurious findings from meta-regression
Higgins, J. P. T., & Thompson, S. G. (2004) · Statistics in Medicine · doi:10.1002/sim.1752 · Cited by 1,267
A new measure of prognostic separation in survival data
Royston, P., & Sauerbrei, W. (2004) · Statistics in Medicine · doi:10.1002/sim.1621 · Cited by 450 · cited 1×
“The last considered the chosen number of simulations to be sufficient, as they were not aiming to estimate any quantities with high accuracy [ 38 ].”
BackgroundSelf-citation
Cited by
Teare, M. D., Dimairo, M., Shephard, N., Hayman, A., Whitehead, A., & Walters, S. J. (2014) · Trials · doi:10.1186/1745-6215-15-264 · Cited by 773
Collins, G. S., Ogundimu, E. O., & Altman, D. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6787 · Cited by 665
Self-citationCorrecting for Bias in Psychology: A Comparison of Meta-Analytic Methods
Carter, E. C., Schönbrodt, F. D., Gervais, W. M., & Hilgard, J. (2019) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245919847196 · Cited by 588
Twisk, J., de Boer, M., de Vente, W., & Heymans, M. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.03.017 · Cited by 568
Adequate sample size for developing prediction models is not simply related to events per variable
Ogundimu, E. O., Altman, D. G., & Collins, G. S. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.02.031 · Cited by 546
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Metadata
- Authors
- Burton, A., Altman, D. G., Royston, P., & Holder, R. L.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2006-08-31
- Volume
- 25
- Issue
- 24
- Pages
- 4279-4292
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2095967516
- Cited by
- 752
- References
- 7
- FWCI
- 8.54
- Type
- journal article
- Language
- en
- Open access
- closed
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