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How to check a simulation study

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  • Ian R White

    (MRC Clinical Trials Unit at UCL, London, UK)

  • Tra My Pham

    (MRC Clinical Trials Unit at UCL, London, UK)

  • Matteo Quartagno

    (MRC Clinical Trials Unit at UCL, London, UK)

  • Tim P Morris

    (MRC Clinical Trials Unit at UCL, London, UK)

Abstract

Simulation studies are a powerful tool in biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings where answers are already known. Code should be written in stages and data generating mechanisms should be checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates a surprisingly powerful tool. When estimation fails or there are outlying estimates, these should be identified, understood, and dealt with by changing data generating mechanisms or coding realistic hybrid analysis procedures. Finally, we give a series of ideas that have been useful to us in the past for checking unexpected results. Following our advice may help to prevent errors and to improve the quality of published simulation studies. We illustrate the ideas with a simple but realistic simulation study in Stata.

Suggested Citation

  • Ian R White & Tra My Pham & Matteo Quartagno & Tim P Morris, 2023. "How to check a simulation study," UK Stata Conference 2023 16, Stata Users Group.
    • Handle: RePEc:boc:lsug23:16
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    File URL: http://repec.org/lsug2023/Stata_UK23_Pham.pptx
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    1. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
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