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Correlations between estimated and true dietary intakes: Using two instrumental variables

    Research output: Contribution to journal › Article › peer-review

    Abstract

    PURPOSE: We describe a new application of the method of triads that allows an estimate of the correlation between a dietary questionnaire measure (Q) and true intake (T). METHODS: Three surrogate variables Q, M, and P are observed where M and P are both instrumental (often biological) variables. A reference dietary method (R) is not required. The variables M and P may be concentration rather than recovery biomarkers. Estimating equations produce Cor̂r(Q,T), Cor̂r(M,T), Cor̂r(P,T), conditional on assumptions about error correlations. Correlations between errors in both Q and a reference dietary measure can also be estimated if R is available. A small validation study of California Seventh-day Adventists provided food frequency, repeated 24-hour dietary recalls (R), and biological data (blood, overnight urines, and subcutaneous fat). RESULTS: Values of Cor̂r(Q,T) ranged between 0.40 and 0.66. Values of Cor̂r(R,T) were higher, between 0.48 and 0.83. Estimated correlations between errors in R and Q were all positive. CONCLUSIONS: When carefully chosen, M and P, rather than M and R, should better satisfy assumptions about error correlations. Food frequency data and repeated 24-hour recalls both provide estimates of T, but the latter has greater validity. Standard errors suggest that for good precision Cor̂r(Q,T) requires large validation studies (2000-3000 subjects). © 2005 Elsevier Inc. All rights reserved.
    Original languageEnglish
    Pages (from-to)509-518
    Number of pages10
    JournalAnnals of Epidemiology
    Volume15
    Issue number7
    DOIs
    StatePublished - Aug 2005

    ASJC Scopus Subject Areas

    • Epidemiology

    Keywords

    • Food Frequency Question
    • Latent Variable
    • Structural Models
    • Validity
    • Diet Surveys
    • Humans
    • Middle Aged
    • Self-Assessment
    • Male
    • California
    • Models, Statistical
    • Biomarkers
    • Adult
    • Female
    • Perception
    • Protestantism
    • Surveys and Questionnaires
    • Aged
    • Nutritional Status

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