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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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John Wiley and Sons Inc.
2020
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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. |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7362680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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