Use of two-part regression calibration model to correct for measurement error in episodically consumed foods in a single-replicate study design: EPIC case study.
In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.
National Institute for Public Health and the Environment, Bilthoven, The Netherlands; Biometris, Wageningen University and Research Center, Wageningen, The Netherlands.
George O
GO
Agogo
Biometris, Wageningen University and Research Center, Wageningen, The Netherlands.
Hilko
H
van der Voet
Department of Human Nutrition, Wageningen University and Research Center, Wageningen, The Netherlands.
Pieter
P
van't Veer
Nutritional Epidemiology Group, International Agency for Research on Cancer, Lyon, France.
Pietro
P
Ferrari
Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, The Netherlands.
Max
M
Leenders
Genetic Epidemiology Group, International Agency for Research on Cancer, 150 cours Albert Thomas, Lyon, 69008, France.
David C
DC
Muller
Andalusian School of Public Health, Granada, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
Emilio
E
Sánchez-Cantalejo
WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece.
Christina
C
Bamia
Department of Community Medicine, University of Tromsø, N-9037, Tromsø, Norway.
Tonje
T
Braaten
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Potsdam, Germany.
Sven
S
Knüppel
Department of Odontology, Umeå University, Umeå, Sweden.
Ingegerd
I
Johansson
Biometris, Wageningen University and Research Center, Wageningen, The Netherlands.
Fred A
FA
van Eeuwijk
National Institute for Public Health and the Environment, Bilthoven, The Netherlands; Biometris, Wageningen University and Research Center, Wageningen, The Netherlands; Department of Human Nutrition, Wageningen University and Research Center, Wageningen, The Netherlands.
Hendriek
H
Boshuizen
D000328:Adult; D000368:Aged; D002138:Calibration; D015930:Diet Records; D005060:Europe / Q000453:epidemiology; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology / Q000401:mortality / Q000503:physiopathology*; D015596:Nutrition Assessment; D011446:Prospective Studies; D012044:Regression Analysis; D011795:Surveys and Questionnaires; D015996:Survival Rate