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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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Autores principales: Boulesteix, Anne-Laure, Groenwold, Rolf HH, Abrahamowicz, Michal, Binder, Harald, Briel, Matthias, Hornung, Roman, Morris, Tim P, Rahnenführer, Jörg, Sauerbrei, Willi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737058/
https://www.ncbi.nlm.nih.gov/pubmed/33318113
http://dx.doi.org/10.1136/bmjopen-2020-039921
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author Boulesteix, Anne-Laure
Groenwold, Rolf HH
Abrahamowicz, Michal
Binder, Harald
Briel, Matthias
Hornung, Roman
Morris, Tim P
Rahnenführer, Jörg
Sauerbrei, Willi
author_facet Boulesteix, Anne-Laure
Groenwold, Rolf HH
Abrahamowicz, Michal
Binder, Harald
Briel, Matthias
Hornung, Roman
Morris, Tim P
Rahnenführer, Jörg
Sauerbrei, Willi
author_sort Boulesteix, Anne-Laure
collection PubMed
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.
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spelling pubmed-77370582020-12-28 Introduction to statistical simulations in health research Boulesteix, Anne-Laure Groenwold, Rolf HH Abrahamowicz, Michal Binder, Harald Briel, Matthias Hornung, Roman Morris, Tim P Rahnenführer, Jörg Sauerbrei, Willi BMJ Open Epidemiology 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. BMJ Publishing Group 2020-12-13 /pmc/articles/PMC7737058/ /pubmed/33318113 http://dx.doi.org/10.1136/bmjopen-2020-039921 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Epidemiology
Boulesteix, Anne-Laure
Groenwold, Rolf HH
Abrahamowicz, Michal
Binder, Harald
Briel, Matthias
Hornung, Roman
Morris, Tim P
Rahnenführer, Jörg
Sauerbrei, Willi
Introduction to statistical simulations in health research
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
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737058/
https://www.ncbi.nlm.nih.gov/pubmed/33318113
http://dx.doi.org/10.1136/bmjopen-2020-039921
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