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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

European Journal of Clinical Nutrition volume 63, pages S179–S187 (2009)Cite this article

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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References

  • Bingham SA, Gill C, Welch A, Cassidy A, Runswick SA, Oakes S et al. (1997). Validation of dietary assessment methods in the UK arm of EPIC using weighed records, and 24-hour urinary nitrogen and potassium and serum vitamin C and carotenoids as biomarkers. Int J Epidemiol 26 (Suppl 1), S137–S151.

    Article  Google Scholar 

  • Bingham SA, Williams R, Cole TJ, Price CP, Cummings JH (1988). Reference values for analytes of 24-h urine collections known to be complete. Ann Clin Biochem 25, 610–619.

    Article  CAS  Google Scholar 

  • Cohen A (1989). Comparison of correlated correlations. Stat Med 8, 1485–1495.

    Article  CAS  Google Scholar 

  • Day N, McKeown N, Wong M, Welch A, Bingham S (2001). Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. Int J Epidemiol 30, 309–317.

    Article  CAS  Google Scholar 

  • Day NE, Ferrari P (2002). Some methodological issues in nutritional epidemiology. In: Riboli E, Lambert R (eds). Nutrition and Lifestyle: Opportunities for Cancer Prevention. IARC Sci Publ No 156. International Agency for Research on Cancer: Lyon. pp 5–10.

    Google Scholar 

  • Day NE, Wong MY, Bingham S, Khaw KT, Luben R, Michels KB et al. (2004). Correlated measurement error—implications for nutritional epidemiology. Int J Epidemiol 33, 1373–1381.

    Article  CAS  Google Scholar 

  • Efron B, Tibshirani RJ (1998). An Introduction to the Bootstrap. Chapman & Hall: London.

    Google Scholar 

  • Ferrari P, Day NE, Boshuizen HC, Roddam A, Hoffmann K, Thiébaut A et al. (2008). The evaluation of the diet/disease relation in the EPIC study: considerations for the calibration and the disease models. Int J Epidemiol 37, 379–381.

    Article  Google Scholar 

  • Ferrari P, Friedenreich C, Matthews C (2007). The role of measurement error in estimating levels of physical activity. Am J Epidemiol 166, 832–844.

    Article  Google Scholar 

  • Ferrari P, Kaaks R, Fahey MT, Slimani N, Day NE, Pera G et al. (2004). Within- and between-cohort variation in measured macronutrient intakes, taking account of measurement errors, in the European Prospective Investigation into Cancer and Nutrition study. Am J Epidemiol 160, 814–822.

    Article  Google Scholar 

  • Ferrari P, Slimani N, Ciampi A, Trichopoulou A, Naska A, Lauria C et al. (2002). Evaluation of under- and overreporting of energy intake in the 24-hour diet recalls in the European Prospective Investigation into Cancer and Nutrition (EPIC). Public Health Nutr 5 (Suppl), S1329–S1345.

    Article  Google Scholar 

  • Fisher RA (1970). Statistical Methods for Research Workers, 14th edn. Hafner Publishing Company: Davien, CT.

    Google Scholar 

  • Freedman LS, Schatzkin A, Wax Y (1990). The impact of dietary measurement error on planning sample size required in a cohort study. Am J Epidemiol 132, 1185–1195.

    Article  CAS  Google Scholar 

  • Heitmann BL, Lissner L (1995). Dietary underreporting by obese individuals—is it specific or non- specific? Br Med J 311, 986–989.

    Article  CAS  Google Scholar 

  • Johansson G, Bingham S, Vahter M (1999). A method to compensate for incomplete 24-hour urine collections in nutritional epidemiology studies. Public Health Nutr 2, 587–591.

    Article  CAS  Google Scholar 

  • Kaaks R, Ferrari P, Ciampi A, Plummer M, Riboli E (2002). Uses and limitations of statistical accounting for random error correlations in the validation of dietary questionnaire assessments. Public Health Nutr 5, 969–976.

    Article  Google Scholar 

  • Kaaks R, Plummer M, Riboli E, Esteve J, van Staveren W (1994). Adjustment for bias due to errors in exposure assessments in multicenter cohort studies on diet and cancer: a calibration approach. Am J Clin Nutr 59 (Suppl 1), S245–S250.

    Article  Google Scholar 

  • Kipnis V, Freedman LS, Brown CC, Hartman AM, Schatzkin A, Wacholder S (1997). Effect of measurement error on energy-adjustment models in nutritional epidemiology. Am J Epidemiol 146, 842–855.

    Article  CAS  Google Scholar 

  • Kipnis V, Subar AF, Midthune D, Freedman LS, Ballard-Barbash R, Troiano RP et al. (2003). Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol 158, 14–21.

    Article  Google Scholar 

  • Kristal AR, Peters U, Potter JD (2005). Is it time to abandon the food frequency questionnaire? Cancer Epidemiol Biomarkers Prev 14, 2826–2828.

    Article  Google Scholar 

  • Plummer M, Clayton D (1993). Measurement error in dietary assessment: an investigation using covariance structure models, Part I. Stat Med 12, 925–935.

    Article  CAS  Google Scholar 

  • Prentice R (1996). Measurement error and results from analytic epidemiology: dietary fat and breast cancer. J Natl Cancer Inst 88, 1738–1747.

    Article  CAS  Google Scholar 

  • Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, Fahey M et al. (2002). European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr 5 (Suppl), S1113–S1124.

    Article  Google Scholar 

  • Riboli E, Kaaks R (2000). Invited commentary: the challenge of multi-center cohort studies in the search for diet and cancer links. Am J Epidemiol 151, 371–376.

    Article  CAS  Google Scholar 

  • Rosner B, Spiegelman D, Willett WC (1990). Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am J Epidemiol 132, 734–745.

    Article  CAS  Google Scholar 

  • Rosner B, Willett WC, Spiegelman D (1989). Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med 8, 1051–1069.

    Article  CAS  Google Scholar 

  • SAS Institute (1999). SAS Online Doc, Version 8. SAS Institute Inc.: Cary, NC.

  • Schatzkin A, Kipnis V, Carroll RJ, Midthune D, Subar AF, Bingham S et al. (2003). A comparison of a food frequency questionnaire with a 24-hour recall for use in an epidemiological cohort study: results from the biomarker-based Observing Protein and Energy Nutrition (OPEN) study. Int J Epidemiol 32, 1054–1062.

    Article  Google Scholar 

  • Slimani N, Bingham SJ, Runswick S, Ferrari P, Day NE, Welch AA et al. (2003). Group level validation of protein intakes estimated by 24-hour diet recall and dietary questionnaires against 24-hour urinary nitrogen in the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study. Cancer Epidemiol Biomarkers Prev 12, 784–795.

    PubMed  Google Scholar 

  • Slimani N, Deharveng G, Unwin I, Southgate DA, Vignat J, Skeie G et al. (2007). The EPIC nutrient database project (ENDB): a first attempt to standardize nutrient databases across the 10 European countries participating in the EPIC study. Eur J Clin Nutr 61, 1037–1056.

    Article  CAS  Google Scholar 

  • Slimani N, Ferrari P, Ocké M, Welch A, Boeing H, van Liere M et al. (2000). Standardisation of the 24-hour diet recall calibration method used in the European Prospective Investigation into Cancer and Nutrition (EPIC): general concepts and preliminary results. Eur J Clin Nutr 54, 900–917.

    Article  CAS  Google Scholar 

  • Slimani N, Kaaks R, Ferrari P, Casagrande C, Clavel-Chapelon F, Lotze G et al. (2002). European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study: rationale, design and population characteristics. Public Health Nutr 5 (Suppl), S1125–S1145.

    Article  Google Scholar 

  • Stram DO, Hankin JH, Wilkens LR, Pike MC, Monroe KR, Park S et al. (2000). Calibration of the dietary questionnaire for a multiethnic cohort in Hawaii and Los Angeles. Am J Epidemiol 151, 358–370.

    Article  CAS  Google Scholar 

  • Tasevska N, Runswick SA, Bingham SA (2006). Urinary potassium is as reliable as urinary nitrogen for use as a recovery biomarker in dietary studies of free living individuals. J Nutr 136, 1334–1340.

    Article  Google Scholar 

  • Thompson FE, Moler JE, Freedman LS, Clifford CK, Stables GJ, Willett WC (1997). Register of dietary assessment calibration-validation studies: a status report. Am J Clin Nutr 65 (Suppl 4), S1142–S1147.

    Article  Google Scholar 

  • Willett W (2001). Commentary: dietary diaries versus food frequency questionnaires—a case of undigestible data. Int J Epidemiol 30, 317–319.

    Article  CAS  Google Scholar 

Download references

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.

Author information

Author notes

  1. P Ferrari

    Present address: 25Current address: Data Collection and Exposure Unit (DATEX), European Food Safety Authority, Parma, Italy.,

  2. J Linseisen

    Present address: 26Current address: Institute of Epidemiology, Helmholtz Centre Munich, Neuherberg, Germany.,

  3. A A Welch

    Present address: 27Current address: School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK.,

  4. S Bingham: The author is deceased.

Authors and Affiliations

  1. Dietary Exposure Assessment Group, International Agency for Research on Cancer, Lyon, France

    P Ferrari, C Biessy, S Rinaldi, I Huybrechts & N Slimani

  2. Cancer Epidemiology Unit, University of Oxford, Oxford, UK

    A Roddam

  3. Biostatistics Unit, Medical Research Council and University of Cambridge, Cambridge, UK

    M T Fahey

  4. Lifestyle and Cancer Group, International Agency for Research on Cancer, Lyon, France

    M Jenab

  5. Department of Hygiene Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece

    C Bamia, E Oikonomou & P Orfanos

  6. National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands

    M Ocké & H B Bueno-de-Mesquita

  7. Public Health Division of Gipuzkoa, Basque Government, San Sebastian and CIBER Epidemiología y Salud Pública (CIBERESP), Spain

    P Amiano & M Dorronsoro

  8. Cancer Registry of Norway, Oslo, Norway

    A Hjartåker

  9. Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark

    A Tjønneland

  10. Department of Cardiology, Center for Cardiovascular Research, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark

    C Dethlefsen

  11. Inserm, ERI 20, Institut Gustave Roussy, Villejuif, France

    M Niravong & F Clavel-Chapelon

  12. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    J Linseisen

  13. Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbrücke, Germany

    H Boeing

  14. Molecular and Nutritional Epidemiology Unit, ISPO, Florence, Italy

    D Palli

  15. Department of Clinical and Experimental Medicine, University of Naples, Federico II, Naples, Italy

    M Santucci de Magistris

  16. Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands

    P H M Peeters

  17. Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway

    C L Parr

  18. Institute of Community Medicine, University of Tromsø, Tromsø, Norway

    T Braaten

  19. Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Barcelona, Spain

    T Berenguer

  20. Department of Clinical Sciences, Lund University, Malmö, Sweden

    B Gullberg

  21. Department of Odontology, Faculty of Medicine, University of Umeå, Umeå, Sweden

    I Johansson

  22. Department of Public Health and Primary Care, MRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival, University of Cambridge, Cambridge, UK

    A A Welch & S Bingham

  23. Department of Epidemiology, Public Health and Primary Care, Imperial College, London, UK

    E Riboli

  24. Diet and Cancer Group, MRC Mitochondrial Biology Unit, Cambridge, UK

    S Bingham

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  1. P Ferrari
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  30. A A Welch
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  32. S Bingham
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  33. N Slimani
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Corresponding author

Correspondence to N Slimani.

Additional information

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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