Abstract
Objectives:
Within the European Prospective Investigation into Cancer and Nutrition (EPIC) study, the performance of 24-h dietary recall (24-HDR) measurements as reference measurements in a linear regression calibration model is evaluated critically at the individual (within-centre) and aggregate (between-centre) levels by using unbiased estimates of urinary measurements of nitrogen and potassium intakes.
Methods:
Between 1995 and 1999, 1072 study subjects (59% women) from 12 EPIC centres volunteered to collect 24-h urine samples. Log-transformed questionnaire, 24-HDR and urinary measurements of nitrogen and potassium intakes were analysed in a multivariate measurement error model to estimate the validity of coefficients and error correlations in self-reported dietary measurements. In parallel, correlations between means of 24-HDR and urinary measurements were computed. Linear regression calibration models were used to estimate the regression dilution (attenuation) factors.
Results:
After adjustment for sex, centre, age, body mass index and height, the validity coefficients for 24-HDRs were 0.285 (95% confidence interval: 0.194, 0.367) and 0.371 (0.291, 0.446) for nitrogen and potassium intakes, respectively. The attenuation factors estimated in a linear regression calibration model were 0.368 (0.228, 0.508) for nitrogen and 0.500 (0.361, 0.639) for potassium intakes; only the former was different from the estimate obtained using urinary measurements in the measurement error model. The aggregate-level correlation coefficients between means of urinary and 24-HDR measurements were 0.838 (0.637, 0.932) and 0.756 (0.481, 0.895) for nitrogen and potassium intakes, respectively.
Conclusions:
This study suggests that 24-HDRs can be used as reference measurements at the individual and aggregate levels for potassium intake, whereas, for nitrogen intake, good performance is observed for between-centre calibration, but some limitations are apparent at the individual level.
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Acknowledgements
The work described in this paper was carried out with the financial support of the European Commission: Public Health and Consumer Protection Directorate 1993–2004; Research Directorate-General 2005; Ligue contre le Cancer (France); Société 3M (France); Mutuelle Générale de l’Education Nationale; Institut National de la Santé et de la Recherche Médicale (INSERM); Institut Gustave Roussy; German Cancer Aid; German Cancer Research Center; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health; Spanish Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra and the Catalan Institute of Oncology; and ISCIII RETIC (RD06/0020), Spain; Cancer Research UK; Medical Research Council, UK; The Stroke Association, UK; British Heart Foundation; Department of Health, UK; Food Standards Agency, UK; The Wellcome Trust, UK; Greek Ministry of Health; Hellenic Health Foundation; Italian Association for Research on Cancer; Italian National Research Council, Regione Sicilia (Sicilian government); Associazione Iblea per la Ricerca Epidemiologica—ONLUS (Hyblean Association for Epidemiological Research, NPO); Dutch Ministry of Health, Welfare and Sport; Dutch Prevention Funds; LK Research Funds; Dutch ZON (Zorg Onderzoek Nederland); World Cancer Research Fund (WCRF); Swedish Cancer Society; Swedish Research Council; Regional Government of Skane and the County Council of Vasterbotten, Sweden; Norwegian Cancer Society; the Norwegian Research Council and the Norwegian Foundation for Health and Rehabilitation. We thank Sarah Somerville, Nicole Suty and Karima Abdedayem for their assistance with editing, and Kimberley Bouckaert and Heinz Freisling for their technical assistance.
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Guarantor: P Ferrari.
Contributors: PF conducted the statistical analysis, prepared the tables and wrote the paper, taking into account comments from all co-authors. NS was the overall coordinator of this project and the EPIC Nutrient Database (ENDB) project. AR, MF, MJ, CB, MO, PA, AH and CB were members of the writing group and gave input on the statistical analyses, drafting of the paper and interpretation of results. The other co-authors were local EPIC collaborators who participated in the collection of dietary and other data and in the EPIC nutritional database (ENDB) project. ER is the overall coordinator of the EPIC study. All co-authors provided comments and suggestions on the manuscript and approved the final version.
Appendix
Appendix
Consider a bivariate linear regression model of the relationship between a vector of true exposure, T=(T1, T2), and the vector of observed exposure, Q=(Q1, Q2). For simplicity, if we consider the first exposure and omit any confounding variables, then the relationship has the form T1=λ10+λ11Q1+λ12Q2+ɛ1. The elements of the vector of parameters λ1=(λ11, λ12) consist of the attenuation factor for the first exposure and the contamination factor for the second exposure, respectively. Let ΣAB be the covariance matrix between random variables A and B; then λ1 can be estimated as
Elements in matrices ΣQQ and ΣQT1 can be expressed as a function of parameters estimated from the measurement error model (1). For example, the variance between T1 and Q1 can be calculated by σQ 1 T 1=β̂Q 1ς̂T 12+ς̂ɛ Q12. The term ‘ρQ1Q2 ’ represents the correlation between Q1 and Q2. Unlike the linear regression calibration model in (2), which uses R (instead of T), Q and Zk1 variables, estimates of ΣQT 1 obtained from model (1) are not affected by error correlations in questionnaire and 24-HDR measurements. An identical approach is applied for the vector λ2 for the second error-prone variable, noting that T2=λ20+λ21Q1+λ22Q2+ɛ2.
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Ferrari, P., Roddam, A., Fahey, M. et al. A bivariate measurement error model for nitrogen and potassium intakes to evaluate the performance of regression calibration in the European Prospective Investigation into Cancer and Nutrition study. Eur J Clin Nutr 63 (Suppl 4), S179–S187 (2009). https://doi.org/10.1038/ejcn.2009.80
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DOI: https://doi.org/10.1038/ejcn.2009.80
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