Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems.
Publication
, Journal Article
Samsa, GP
Published in: Learn Health Syst
July 2020
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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Published In
Learn Health Syst
DOI
EISSN
2379-6146
Publication Date
July 2020
Volume
4
Issue
3
Start / End Page
e10219
Location
United States
Citation
APA
Chicago
ICMJE
MLA
NLM
Samsa, G. P. (2020). Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems. Learn Health Syst, 4(3), e10219. https://doi.org/10.1002/lrh2.10219
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.” Learn Health Syst 4, no. 3 (July 2020): e10219. https://doi.org/10.1002/lrh2.10219.
Samsa GP. Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems. Learn Health Syst. 2020 Jul;4(3):e10219.
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.” Learn Health Syst, vol. 4, no. 3, July 2020, p. e10219. Pubmed, doi:10.1002/lrh2.10219.
Samsa GP. Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems. Learn Health Syst. 2020 Jul;4(3):e10219.
Published In
Learn Health Syst
DOI
EISSN
2379-6146
Publication Date
July 2020
Volume
4
Issue
3
Start / End Page
e10219
Location
United States