Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes
Buckman, Dennis W.
Dodd, Kevin W.
Guenther, Patricia M.
Krebs-Smith, Susan M.
Subar, Amy F.
Tooze, Janet A.
Carroll, Raymond J.
Freedman, Laurence S.
KAUST Grant NumberKUS-CI-016-04
MetadataShow full item record
AbstractDietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575-1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food's usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America's Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.
CitationKipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, et al. (2009) Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes. Biometrics 65: 1003–1010. Available: http://dx.doi.org/10.1111/j.1541-0420.2009.01223.x.
SponsorsR.J.C.’s research was supported by a grant from the NationalCancer Institute (CA 57030) and by Award KUS-CI-016-04,made by King Abdullah University of Science and Technology.
PubMed Central IDPMC2881223
CollectionsPublications Acknowledging KAUST Support
- A zero-augmented generalized gamma regression calibration to adjust for covariate measurement error: A case of an episodically consumed dietary intake.
- Authors: Agogo GO
- Issue date: 2017 Jan
- A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution.
- Authors: Tooze JA, Midthune D, Dodd KW, Freedman LS, Krebs-Smith SM, Subar AF, Guenther PM, Carroll RJ, Kipnis V
- Issue date: 2006 Oct
- The food propensity questionnaire: concept, development, and validation for use as a covariate in a model to estimate usual food intake.
- Authors: Subar AF, Dodd KW, Guenther PM, Kipnis V, Midthune D, McDowell M, Tooze JA, Freedman LS, Krebs-Smith SM
- Issue date: 2006 Oct
- 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.
- Authors: Agogo GO, van der Voet H, van't Veer P, Ferrari P, Leenders M, Muller DC, Sánchez-Cantalejo E, Bamia C, Braaten T, Knüppel S, Johansson I, van Eeuwijk FA, Boshuizen H
- Issue date: 2014
- Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology.
- Authors: Carroll RJ, Midthune D, Subar AF, Shumakovich M, Freedman LS, Thompson FE, Kipnis V
- Issue date: 2012 Feb 15