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Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems

We consider four memes, correct within the context of randomized trials but requiring modification for the analysis of the observational databases typically associated with learning health systems: (a) the right answer always requires randomization; (b) a bigger database is always a better database;...

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Detalles Bibliográficos
Autor principal: Samsa, Gregory P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362680/
https://www.ncbi.nlm.nih.gov/pubmed/32685686
http://dx.doi.org/10.1002/lrh2.10219
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author Samsa, Gregory P.
author_facet Samsa, Gregory P.
author_sort Samsa, Gregory P.
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description We consider four memes, correct within the context of randomized trials but requiring modification for the analysis of the observational databases typically associated with learning health systems: (a) the right answer always requires randomization; (b) a bigger database is always a better database; (c) statistical adjustment always works if based on a large enough database; and (d) always make a formal adjustment when testing multiple hypotheses. The rationale for these memes within the context of randomized trials is discussed, and the memes are restated in a fashion that is consistent with learning health systems.
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spelling pubmed-73626802020-07-17 Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems Samsa, Gregory P. Learn Health Syst Commentary We consider four memes, correct within the context of randomized trials but requiring modification for the analysis of the observational databases typically associated with learning health systems: (a) the right answer always requires randomization; (b) a bigger database is always a better database; (c) statistical adjustment always works if based on a large enough database; and (d) always make a formal adjustment when testing multiple hypotheses. The rationale for these memes within the context of randomized trials is discussed, and the memes are restated in a fashion that is consistent with learning health systems. John Wiley and Sons Inc. 2020-02-12 /pmc/articles/PMC7362680/ /pubmed/32685686 http://dx.doi.org/10.1002/lrh2.10219 Text en © 2020 The Author. Learning Health Systems published by Wiley Periodicals, Inc. on behalf of the University of Michigan This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Commentary
Samsa, Gregory P.
Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems
title Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems
title_full Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems
title_fullStr Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems
title_full_unstemmed Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems
title_short Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems
title_sort some statistical memes which sound correct but aren't quite: application to the analysis of observational databases used in learning health systems
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362680/
https://www.ncbi.nlm.nih.gov/pubmed/32685686
http://dx.doi.org/10.1002/lrh2.10219
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