Introduction to statistical simulations in health research
In health research, statistical methods are frequently used to address a wide variety of research questions. For almost every analytical challenge, different methods are available. But how do we choose between different methods and how do we judge whether the chosen method is appropriate for our spe...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2020-12-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/10/12/e039921.full |
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| author | Matthias Briel Ewout Steyerberg Pamela Shaw Tim P Morris Harald Binder Michal Abrahamowicz Anne-Laure Boulesteix Rolf HH Groenwold Roman Hornung Jörg Rahnenführer Willi Sauerbrei Victor Kipnis Jessica Myers Franklin Ingeborg Waernbaum |
| author_facet | Matthias Briel Ewout Steyerberg Pamela Shaw Tim P Morris Harald Binder Michal Abrahamowicz Anne-Laure Boulesteix Rolf HH Groenwold Roman Hornung Jörg Rahnenführer Willi Sauerbrei Victor Kipnis Jessica Myers Franklin Ingeborg Waernbaum |
| author_sort | Matthias Briel |
| collection | DOAJ |
| description | In health research, statistical methods are frequently used to address a wide variety of research questions. For almost every analytical challenge, different methods are available. But how do we choose between different methods and how do we judge whether the chosen method is appropriate for our specific study? Like in any science, in statistics, experiments can be run to find out which methods should be used under which circumstances. The main objective of this paper is to demonstrate that simulation studies, that is, experiments investigating synthetic data with known properties, are an invaluable tool for addressing these questions. We aim to provide a first introduction to simulation studies for data analysts or, more generally, for researchers involved at different levels in the analyses of health data, who (1) may rely on simulation studies published in statistical literature to choose their statistical methods and who, thus, need to understand the criteria of assessing the validity and relevance of simulation results and their interpretation; and/or (2) need to understand the basic principles of designing statistical simulations in order to efficiently collaborate with more experienced colleagues or start learning to conduct their own simulations. We illustrate the implementation of a simulation study and the interpretation of its results through a simple example inspired by recent literature, which is completely reproducible using the R-script available from online supplemental file 1. |
| format | Article |
| id | doaj-art-0823f07cd2da43cb8798e46081c33a66 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-0823f07cd2da43cb8798e46081c33a662025-08-20T02:18:57ZengBMJ Publishing GroupBMJ Open2044-60552020-12-01101210.1136/bmjopen-2020-039921Introduction to statistical simulations in health researchMatthias Briel0Ewout Steyerberg1Pamela Shaw2Tim P Morris3Harald Binder4Michal Abrahamowicz5Anne-Laure Boulesteix6Rolf HH Groenwold7Roman Hornung8Jörg Rahnenführer9Willi Sauerbrei10Victor KipnisJessica Myers FranklinIngeborg Waernbaum11Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital of Basel, Basel, Switzerland2Leiden University1Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UKmedical statistician6University Medical Center Freiburg, Institute of Medical Biometry and Statistics, Freiburg, GermanyDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, CanadaInstitute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, GermanyDepartment of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The NetherlandsInstitute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, GermanyDepartment of Statistics, TU Dortmund University, Dortmund, Nordrhein-Westfalen, GermanyInstitute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, GermanyDepartment of Statistics, Uppsala University, Uppsala, SwedenIn health research, statistical methods are frequently used to address a wide variety of research questions. For almost every analytical challenge, different methods are available. But how do we choose between different methods and how do we judge whether the chosen method is appropriate for our specific study? Like in any science, in statistics, experiments can be run to find out which methods should be used under which circumstances. The main objective of this paper is to demonstrate that simulation studies, that is, experiments investigating synthetic data with known properties, are an invaluable tool for addressing these questions. We aim to provide a first introduction to simulation studies for data analysts or, more generally, for researchers involved at different levels in the analyses of health data, who (1) may rely on simulation studies published in statistical literature to choose their statistical methods and who, thus, need to understand the criteria of assessing the validity and relevance of simulation results and their interpretation; and/or (2) need to understand the basic principles of designing statistical simulations in order to efficiently collaborate with more experienced colleagues or start learning to conduct their own simulations. We illustrate the implementation of a simulation study and the interpretation of its results through a simple example inspired by recent literature, which is completely reproducible using the R-script available from online supplemental file 1.https://bmjopen.bmj.com/content/10/12/e039921.full |
| spellingShingle | Matthias Briel Ewout Steyerberg Pamela Shaw Tim P Morris Harald Binder Michal Abrahamowicz Anne-Laure Boulesteix Rolf HH Groenwold Roman Hornung Jörg Rahnenführer Willi Sauerbrei Victor Kipnis Jessica Myers Franklin Ingeborg Waernbaum Introduction to statistical simulations in health research BMJ Open |
| title | Introduction to statistical simulations in health research |
| title_full | Introduction to statistical simulations in health research |
| title_fullStr | Introduction to statistical simulations in health research |
| title_full_unstemmed | Introduction to statistical simulations in health research |
| title_short | Introduction to statistical simulations in health research |
| title_sort | introduction to statistical simulations in health research |
| url | https://bmjopen.bmj.com/content/10/12/e039921.full |
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