Agogo, George Ovan der Voet, Hilkovan 't Veer, PieterFerrari, PietroMuller, David CSánchez-Cantalejo, EmilioBamia, ChristinaBraaten, TonjeKnüppel, SvenJohansson, Ingegerdvan Eeuwijk, Fred ABoshuizen, Hendriek C2017-05-292017-05-292016-10-13Agogo 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):139http://hdl.handle.net/10668/2686Background 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.enAttenuation-contamination matrixBayesian MCMCMeasurement errorValidation studyEPIC studyEstudios de validaciónSesgoTeorema de BayesMedical Subject Headings::Health Care::Environment and Public Health::Public Health::Epidemiologic Methods::Statistics as Topic::Probability::Bayes TheoremMedical Subject Headings::Health Care::Environment and Public Health::Public Health::Epidemiologic Factors::Bias (Epidemiology)Medical Subject Headings::Health Care::Health Care Quality, Access, and Evaluation::Quality of Health Care::Health Care Evaluation Mechanisms::Evaluation Studies as Topic::Validation Studies as TopicMedical Subject Headings::Health Care::Environment and Public Health::Public Health::Epidemiologic Methods::Statistics as Topic::Models, Statisticalresearch article27737637Acceso abierto10.1186/s12874-016-0240-11471-2288PMC5064985