Nonparametric Bounds for the Risk Function.
bias
bounds
inference
missing data
Journal
American journal of epidemiology
ISSN: 1476-6256
Titre abrégé: Am J Epidemiol
Pays: United States
ID NLM: 7910653
Informations de publication
Date de publication:
01 04 2019
01 04 2019
Historique:
received:
31
05
2018
revised:
10
01
2019
accepted:
11
01
2019
pubmed:
31
1
2019
medline:
25
12
2019
entrez:
31
1
2019
Statut:
ppublish
Résumé
Nonparametric bounds for the risk difference are straightforward to calculate and make no untestable assumptions about unmeasured confounding or selection bias due to missing data (e.g., dropout). These bounds are often wide and communicate uncertainty due to possible systemic errors. An illustrative example is provided.
Identifiants
pubmed: 30698633
pii: 5304465
doi: 10.1093/aje/kwz013
pmc: PMC6438811
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
632-636Subventions
Organisme : NIAID NIH HHS
ID : K01 AI125087
Pays : United States
Organisme : NIAID NIH HHS
ID : P30 AI050410
Pays : United States
Organisme : NIAID NIH HHS
ID : U01 AI103390
Pays : United States
Organisme : NHLBI NIH HHS
ID : U01 HL146194
Pays : United States
Informations de copyright
© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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