Nonparametric Bounds for the Risk Function

Abstract 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 il...

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Bibliographic Details
Published in:American journal of epidemiology 2019-04, Vol.188 (4), p.632-636
Main Authors: Cole, Stephen R, Hudgens, Michael G, Edwards, Jessie K, Brookhart, M Alan, Richardson, David B, Westreich, Daniel, Adimora, Adaora A
Format: Article
Language:English
Subjects:
Commentaries
Confounding Factors, Epidemiologic
Epidemiologic Research Design
Humans
Missing data
Nonparametric statistics
Risk Assessment - methods
Selection Bias
Statistics, Nonparametric
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Summary:Abstract 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.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kwz013