Berkson's paradox and weighted distributions: An application to Alzheimer's disease.
ABC rejection algorithm
Alzheimer's disease
Berkson's fallacy
biased sampling
likelihood-free inference
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
15
01
2019
revised:
11
09
2019
accepted:
18
09
2019
pubmed:
8
11
2019
medline:
29
12
2020
entrez:
8
11
2019
Statut:
ppublish
Résumé
One reason for observing in practice a false positive or negative correlation between two random variables, which are either not correlated or correlated with a different direction, is the overrepresentation in the sample of individuals satisfying specific properties. In 1946, Berkson first illustrated the presence of a false correlation due to this last reason, which is known as Berkson's paradox and is one of the most famous paradox in probability and statistics. In this paper, the concept of weighted distributions is utilized to describe Berskon's paradox. Moreover, a proper procedure is suggested to make inference for the population given a biased sample which possesses all the characteristics of Berkson's paradox. A real data application for patients with dementia due to Alzheimer's disease demonstrates that the proposed method reveals characteristics of the population that are masked by the sampling procedure.
Identifiants
pubmed: 31696967
doi: 10.1002/bimj.201900046
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
238-249Subventions
Organisme : NIA NIH HHS
ID : U01 AG024904
Pays : United States
Organisme : CIHR
Pays : Canada
Informations de copyright
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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