Document Detail


Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes.
MedLine Citation:
PMID:  19302405     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Dietary 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.
Authors:
Victor Kipnis; Douglas Midthune; Dennis W Buckman; Kevin W Dodd; Patricia M Guenther; Susan M Krebs-Smith; Amy F Subar; Janet A Tooze; Raymond J Carroll; Laurence S Freedman
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Biometrics     Volume:  65     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2009 Dec 
Date Detail:
Created Date:  2009-12-16     Completed Date:  2010-03-04     Revised Date:  2014-09-22    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1003-10     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Animals
Biometry / methods*
Diet Records
Eating*
Female
Fishes
Health Status
Health Surveys
Humans
Mercury / blood
Models, Statistical*
Nutrition Surveys
Questionnaires
Regression Analysis
Grant Support
ID/Acronym/Agency:
CA57030/CA/NCI NIH HHS; R37 CA057030/CA/NCI NIH HHS; R37 CA057030-21/CA/NCI NIH HHS
Chemical
Reg. No./Substance:
FXS1BY2PGL/Mercury
Comments/Corrections

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