Literature DB >> 31145435

Simple Sensitivity Analysis for Differential Measurement Error.

Tyler J VanderWeele1, Yige Li1.   

Abstract

Sensitivity analysis results are given for differential measurement error of either the exposure or outcome. In the case of differential measurement error of the outcome, it is shown that the true effect of the exposure on the outcome on the risk ratio scale must be at least as large as the observed association between the exposure and the mismeasured outcome divided by the maximum strength of differential measurement error. This maximum strength of differential measurement error is itself assessed as the risk ratio of the controlled direct effect of the exposure on the mismeasured outcome not through the true outcome. In the case of differential measurement error of the exposure, under certain assumptions concerning classification probabilities, the true effect on the odds ratio scale of the exposure on the outcome must be at least as large as the observed odds ratio between the mismeasured exposure and the outcome divided by the maximum odds ratio of the effect of the outcome on mismeasured exposure conditional on the true exposure. The results can be immediately used to indicate the minimum strength of differential measurement error that would be needed to explain away an observed association between an exposure measurement and an outcome measurement for this to be solely due to measurement error.
© 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.

Entities:  

Keywords:  bias analysis; differential; measurement error; misclassification; sensitivity analysis

Mesh:

Year:  2019        PMID: 31145435      PMCID: PMC6768812          DOI: 10.1093/aje/kwz133

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


  11 in total

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Journal:  Am J Epidemiol       Date:  1990-10       Impact factor: 4.897

4.  Effects of mismodelling and mismeasuring explanatory variables on tests of their association with a response variable.

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7.  When will nondifferential misclassification of an exposure preserve the direction of a trend?

Authors:  C R Weinberg; D M Umbach; S Greenland
Journal:  Am J Epidemiol       Date:  1994-09-15       Impact factor: 4.897

8.  Sensitivity analysis for misclassification in logistic regression via likelihood methods and predictive value weighting.

Authors:  Robert H Lyles; Ji Lin
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9.  Causal Model of the Association of Social Support With Antepartum Depression: A Marginal Structural Modeling Approach.

Authors:  Qiu-Yue Zhong; Bizu Gelaye; Tyler J VanderWeele; Sixto E Sanchez; Michelle A Williams
Journal:  Am J Epidemiol       Date:  2018-09-01       Impact factor: 4.897

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5.  Are Greenland, Ioannidis and Poole opposed to the Cornfield conditions? A defence of the E-value.

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6.  Multiple imputation to quantify misclassification in observational studies of the cognitively impaired: an application for pain assessment in nursing home residents.

Authors:  Anthony P Nunes; Danni Zhao; William M Jesdale; Kate L Lapane
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