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A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data

Overview of attention for article published in BMC Medical Research Methodology, October 2016
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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9 X users
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Title
A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data
Published in
BMC Medical Research Methodology, October 2016
DOI 10.1186/s12874-016-0240-1
Pubmed ID
Authors

George O. Agogo, Hilko van der Voet, Pieter van ’t Veer, Pietro Ferrari, David C. Muller, Emilio Sánchez-Cantalejo, Christina Bamia, Tonje Braaten, Sven Knüppel, Ingegerd Johansson, Fred A. van Eeuwijk, Hendriek C. Boshuizen

Abstract

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. 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. 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. 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.

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X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 17%
Researcher 4 13%
Student > Ph. D. Student 4 13%
Student > Doctoral Student 3 10%
Professor 3 10%
Other 8 27%
Unknown 3 10%
Readers by discipline Count As %
Medicine and Dentistry 7 23%
Nursing and Health Professions 4 13%
Agricultural and Biological Sciences 2 7%
Economics, Econometrics and Finance 2 7%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 7 23%
Unknown 7 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 November 2016.
All research outputs
#6,262,366
of 23,509,982 outputs
Outputs from BMC Medical Research Methodology
#945
of 2,074 outputs
Outputs of similar age
#93,689
of 321,320 outputs
Outputs of similar age from BMC Medical Research Methodology
#13
of 42 outputs
Altmetric has tracked 23,509,982 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,074 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 321,320 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.