Modeling Data with Excess Zeros and Measurement Error: Application to Evaluating Relationships between Episodically Consumed Foods and Health Outcomes

Authors

  • Victor Kipnis,

    Corresponding author
    1. Biometry, Division of Cancer Prevention, National Cancer Institute, 6130 Executive Boulevard, EPN-3131, Bethesda, Maryland 20892-7354, U.S.A.
      email: kipnisv@mail.nih.gov
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  • Douglas Midthune,

    1. Biometry, Division of Cancer Prevention, National Cancer Institute, 6130 Executive Boulevard, EPN-3131, Bethesda, Maryland 20892-7354, U.S.A.
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  • Dennis W. Buckman,

    1. Information Management Services, Inc., 12501 Prosperity Drive, Silver Spring, Maryland 20904, U.S.A.
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  • Kevin W. Dodd,

    1. Biometry, Division of Cancer Prevention, National Cancer Institute, 6130 Executive Boulevard, EPN-3131, Bethesda, Maryland 20892-7354, U.S.A.
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  • Patricia M. Guenther,

    1. Center for Nutrition Policy and Promotion, U.S. Department of Agriculture, 3101 Park Center Drive, Ste 1034, Alexandria, Virginia 22302, U.S.A.
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  • Susan M. Krebs-Smith,

    1. Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, 6130 Executive Boulevard, EPN-4005, Bethesda, Maryland 20892, U.S.A.
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  • Amy F. Subar,

    1. Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, 6130 Executive Boulevard, EPN-4005, Bethesda, Maryland 20892, U.S.A.
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  • Janet A. Tooze,

    1. Department of Biostatistical Sciences, Wake Forest University, School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157, U.S.A.
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  • Raymond J. Carroll,

    1. Department of Statistics, Texas A&M University, 3143 TAMU, College Station, Texas 77843-3143, U.S.A.
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  • Laurence S. Freedman

    1. Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer 52161, Israel
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email: kipnisv@mail.nih.gov

Abstract

Summary 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 Association106, 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.

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