Literature DB >> 30698633

Nonparametric Bounds for the Risk Function.

Stephen R Cole1, Michael G Hudgens2, Jessie K Edwards1, M Alan Brookhart1,3, David B Richardson1, Daniel Westreich1, Adaora A Adimora1,4.   

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.
© 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.

Keywords:  bias; bounds; inference; missing data

Mesh：

Year:  2019        PMID： 30698633      PMCID： PMC6438811          DOI： 10.1093/aje/kwz013

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN： 0002-9262            Impact factor:   4.897


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