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S. Weisberg, J. Fox (2010)
An R Companion to Applied Regression
A. Hebestreit, T. Intemann, A. Siani, S. Henauw, G. Eiben, Y. Kourides, É. Kovács, L. Moreno, T. Veidebaum, V. Krogh, V. Pala, L. Bogl, M. Hunsberger, C. Börnhorst, I. Pigeot (2017)
Dietary Patterns of European Children and Their Parents in Association with Family Food Environment: Results from the I.Family StudyNutrients, 9
K. Dodd, P. Guenther, L. Freedman, A. Subar, V. Kipnis, D. Midthune, J. Tooze, S. Krebs-Smith (2006)
Statistical methods for estimating usual intake of nutrients and foods: a review of the theory.Journal of the American Dietetic Association, 106 10
C. Börnhorst, I. Huybrechts, I. Huybrechts, A. Hebestreit, V. Krogh, A. Decker, G. Barba, L. Moreno, L. Lissner, M. Tornaritis, H. Loit, D. Molnár, I. Pigeot, I. Pigeot (2014)
Usual energy and macronutrient intakes in 2–9-year-old European childrenInternational Journal of Obesity, 38
V. Kipnis, D. Midthune, D. Buckman, K. Dodd, P. Guenther, S. Krebs-Smith, A. Subar, J. Tooze, Ray Carroll, L. Freedman (2009)
Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health OutcomesBiometrics, 65
J. Cook, L. Stefanski (1994)
Simulation-Extrapolation Estimation in Parametric Measurement Error ModelsJournal of the American Statistical Association, 89
R. Carroll (2006)
Measurement error in nonlinear models: a modern perspective
D. Bates, M. Machler, B. Bolker, Steven Walker (2014)
Fitting Linear Mixed-Effects Models Using lme4Journal of Statistical Software, 67
(2007)
An Introduction to the Bootstrap
A. Hebestreit, M. Wolters, Hannah Jilani, G. Eiben, Valeria Pala (2018)
Web-Based 24-h Dietary Recall: The SACANA ProgramInstruments for Health Surveys in Children and Adolescents
W. Ahrens, A. Siani, R. Adan, S. Henauw, G. Eiben, W. Gwozdz, A. Hebestreit, M. Hunsberger, J. Kaprio, V. Krogh, L. Lissner, D. Moln, L. Moreno, A. Page, L. Reisch, R. Smith, M. Tornaritis, T. Veidebaum, G. Williams, H. Pohlabeln, I. Pigeot (2017)
The Transition from Childhood to Adolescence in European Children – How I.Family Extends the IDEFICS Cohort
G. Agogo (2017)
A zero‐augmented generalized gamma regression calibration to adjust for covariate measurement error: A case of an episodically consumed dietary intakeBiometrical Journal, 59
W. Ahrens, K. Bammann, A. Siani, Kirsten Buchecker, S. Henauw, L. Iacoviello, A. Hebestreit, V. Krogh, L. Lissner, S. Mårild, D. Molnár, L. Moreno, Y. Pitsiladis, L. Reisch, M. Tornaritis, T. Veidebaum, I. Pigeot (2011)
The IDEFICS cohort: design, characteristics and participation in the baseline surveyInternational Journal of Obesity, 35
Jade Freeman, R. Modarres (2006)
Inverse Box–Cox: The power-normal distributionStatistics & Probability Letters, 76
P. Shaw, V. Deffner, R. Keogh, J. Tooze, K. Dodd, H. Küchenhoff, V. Kipnis, L. Freedman (2018)
Epidemiologic analyses with error-prone exposures: review of current practice and recommendations.Annals of epidemiology, 28 11
J. Tooze, D. Midthune, K. Dodd, L. Freedman, S. Krebs-Smith, A. Subar, P. Guenther, R. Carroll, V. Kipnis (2006)
A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution.Journal of the American Dietetic Association, 106 10
A. Liese, J. Crandell, J. Tooze, V. Kipnis, R. Bell, S. Couch, D. Dabelea, T. Crume, E. Mayer‐Davis (2015)
Sugar-sweetened beverage intake and cardiovascular risk factor profile in youth with type 1 diabetes: application of measurement error methodology in the SEARCH Nutrition Ancillary StudyBritish Journal of Nutrition, 114
W. Lederer, H Küchenhoff (2006)
A short introduction to the SIMEX and MCSIMEX, 6
R. Team (2014)
R: A language and environment for statistical computing.MSOR connections, 1
(2018)
STRengthening Analytical Thinking for Observational Studies (STRATOS): Introducing the measurement error and misclassification topic group (TG4)
H. Boeing, B. M. Margetts (2014)
Handbook of epidemiology
Ioanna Yiannakou, M. Singer, Lynn Moore, Paul Jacques (2020)
Nutritional EpidemiologyDefinitions
A. Hebestreit, M. Wolters, H. Jilani, G. Eiben, V. Pala (2018)
Instruments for health surveys in children and adolescents
W. Ahrens, A. Siani, R. Adan, S. De Henauw, G. Eiben, W. Gwozdz, I. Pigeot (2017)
Cohort profile: The transition from childhood to adolescence in European children—How I.Family extends the IDEFICS cohort, 46
Wenqing He, G. Yi, J. Xiong (2007)
Accelerated failure time models with covariates subject to measurement errorStatistics in Medicine, 26
T. Intemann, I. Pigeot, S. Henauw, G. Eiben, L. Lissner, V. Krogh, Katarzyna Dereń, D. Molnár, L. Moreno, P. Russo, A. Siani, I. Sirangelo, M. Tornaritis, T. Veidebaum, V. Pala, O. consortium (2019)
Urinary sucrose and fructose to validate self-reported sugar intake in children and adolescents: results from the I.Family studyEuropean Journal of Nutrition, 58
Helmut Küchenhoff, Raymond Carroll (1997)
Segmented regression with errors in predictors: semi-parametric and parametric methods.Statistics in medicine, 16 1-3
L. Stefanski, J. Cook (1995)
Simulation-Extrapolation: The Measurement Error JackknifeJournal of the American Statistical Association, 90
O. Souverein, A. Dekkers, A. Geelen, J. Haubrock, J. Vries, M. Ocké, U. Harttig, H. Boeing, P. Veer (2011)
Comparing four methods to estimate usual intake distributionsEuropean Journal of Clinical Nutrition, 65
Modelling dietary data, and especially 24‐hr dietary recall (24HDR) data, is a challenge. Ignoring the inherent measurement error (ME) leads to biased effect estimates when the association between an exposure and an outcome is investigated. We propose an adapted simulation extrapolation (SIMEX) algorithm for modelling dietary exposures. For this purpose, we exploit the ME model of the NCI method where we assume the assumption of normally distributed errors of the reported intake on the Box‐Cox transformed scale and of unbiased recalls on the original scale. According to the SIMEX algorithm, remeasurements of the observed data with additional ME are generated in order to estimate the association between the level of ME and the resulting effect estimate. Subsequently, this association is extrapolated to the case of zero ME to obtain the corrected estimate. We show that the proposed method fulfils the key property of the SIMEX approach, that is, that the MSE of the generated data will converge to zero if the ME variance converges to zero. Furthermore, the method is applied to real 24HDR data of the I.Family study to correct the effects of salt and alcohol intake on blood pressure. In a simulation study, the method is compared with the NCI method resulting in effect estimates with either smaller MSE or smaller bias in certain situations. In addition, we found our method to be more informative and easier to implement. Therefore, we conclude that the proposed method is useful to promote the dissemination of ME correction methods in nutritional epidemiology.
Biometrical Journal – Wiley
Published: Jan 1, 2020
Keywords: ; ; ; ;
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