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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2016

Open Access 01-12-2016 | Research article

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

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

Published in: BMC Medical Research Methodology | Issue 1/2016

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Abstract

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.
Appendix
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Metadata
Title
A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data
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
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2016
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-016-0240-1

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