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A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data
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2016 (English)In: BMC Medical Research Methodology, ISSN 1471-2288, E-ISSN 1471-2288, Vol. 16, 139Article in journal (Refereed) Published
Abstract [en]

Background: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data. Methods: We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study. Results: Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations. Conclusions: The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders.

Place, publisher, year, edition, pages
2016. Vol. 16, 139
Keyword [en]
Attenuation-contamination matrix, Bayesian MCMC, EPIC study, Measurement error, Validation study
National Category
Nutrition and Dietetics Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:umu:diva-127603DOI: 10.1186/s12874-016-0240-1ISI: 000385455000002PubMedID: 27737637OAI: oai:DiVA.org:umu-127603DiVA: diva2:1052610
Available from: 2016-12-07 Created: 2016-11-16 Last updated: 2016-12-07Bibliographically approved

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Johansson, Ingegerd
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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