American Journal of Epidemiology Vol. 153, No. 4 : 394-403
Copyright © 2001 by The Johns Hopkins University School of Hygiene and Public Health
ORIGINAL CONTRIBUTIONS |
Empirical Evidence of Correlated Biases in Dietary Assessment Instruments and Its Implications
1 Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD.
2 Department of Mathematics, Statistics and Computer Science, Bar Ilan University, Ramat Gan, Israel.
3 Dunn Human Nutrition Unit, Medical Research Council, Cambridge, United Kingdom.
4 Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD.
5 Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD.
6 Department of Statistics, Texas A&M University, College Station, TX.
Multiple-day food records or 24-hour recalls are currently used as "reference" instruments to calibrate food frequency questionnaires (FFQs) and to adjust findings from nutritional epidemiologic studies for measurement error. The common adjustment is based on the critical requirements that errors in the reference instrument be independent of those in the FFQ and of true intake. When data on urinary nitrogen level, a valid reference biomarker for nitrogen intake, are used, evidence suggests that a dietary report reference instrument does not meet these requirements. In this paper, the authors introduce a new model that includes, for both the FFQ and the dietary report reference instrument, group-specific biases related to true intake and correlated person-specific biases. Data were obtained from a dietary assessment validation study carried out among 160 women at the Dunn Clinical Nutrition Center, Cambridge, United Kingdom, in 1988–1990. Using the biomarker measurements and dietary report measurements from this study, the authors compare the new model with alternative measurement error models proposed in the literature and demonstrate that it provides the best fit to the data. The new model suggests that, for these data, measurement error in the FFQ could lead to a 51% greater attenuation of true nutrient effect and the need for a 2.3 times larger study than would be estimated by the standard approach. The implications of the results for the ability of FFQ-based epidemiologic studies to detect important diet-disease associations are discussed. Am J Epidemiol 2001;153:394–403.
biological markers; dietary assessment methods; epidemiologic methods; measurement error; models, statistical; model selection; regression analysis; research design
Abbreviations: AIC, Akaike Information Criterion; BIC, Bayes Information Criterion; FFQ, food frequency questionnaire
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