RT Journal Article
T1 A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data
A1 Agogo, George O
A1 van der Voet, Hilko
A1 van 't Veer, Pieter
A1 Ferrari, Pietro
A1 Muller, David C
A1 Sánchez-Cantalejo, Emilio
A1 Bamia, Christina
A1 Braaten, Tonje
A1 Knüppel, Sven
A1 Johansson, Ingegerd
A1 van Eeuwijk, Fred A
A1 Boshuizen, Hendriek C
K1 Attenuation-contamination matrix
K1 Bayesian MCMC
K1 Measurement error
K1 Validation study
K1 EPIC study
K1 Estudios de validación
K1 Sesgo
K1 Teorema de Bayes
AB BackgroundMeasurement 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.MethodsWe 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.ResultsUsing 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.ConclusionsThe 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.
PB Biomed Central
YR 2016
FD 2016-10-13
LK http://hdl.handle.net/10668/2686
UL http://hdl.handle.net/10668/2686
LA en
NO Agogo GO, van der Voet H, van 't Veer P, Ferrari P, Muller DC, Sánchez-Cantalejo E, et al. A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data. BMC Med Res Methodol. 2016 Oct 13;16(1):139
DS RISalud
RD Apr 20, 2025