Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-26T15:51:15.472Z Has data issue: false hasContentIssue false
  • English
  • Français

Uses and limitations of statistical accounting for random error correlations, in the validation of dietary questionnaire assessments

Published online by Cambridge University Press:  22 December 2006

Rudolf Kaaks ,
Pietro Ferrari ,
Antonio Ciampi ,
Martyn Plummer  and
Elio Riboli
Rudolf Kaaks*
Affiliation:
International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
Pietro Ferrari
Affiliation:
International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
Antonio Ciampi
Affiliation:
Department of Epidemiology and Biostatistics, McGill University, 1020 Pines Avenue West, Montreal, Quebec, Canada
Martyn Plummer
Affiliation:
International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
Elio Riboli
Affiliation:
International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France
*
*Corresponding author: Email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Objective:

To examine statistical models that account for correlation between random errors of different dietary assessment methods, in dietary validation studies.

Setting:

In nutritional epidemiology, sub-studies on the accuracy of the dietary questionnaire measurements are used to correct for biases in relative risk estimates induced by dietary assessment errors. Generally, such validation studies are based on the comparison of questionnaire measurements (Q) with food consumption records or 24-hour diet recalls (R). In recent years, the statistical analysis of such studies has been formalised more in terms of statistical models. This made the need of crucial model assumptions more explicit. One key assumption is that random errors must be uncorrelated between measurements Q and R, as well as between replicate measurements R1 and R2 within the same individual. These assumptions may not hold in practice, however. Therefore, more complex statistical models have been proposed to validate measurements Q by simultaneous comparisons with measurements R plus a biomarker M, accounting for correlations between the random errors of Q and R.

Conclusions:

The more complex models accounting for random error correlations may work only for validation studies that include markers of diet based on physiological knowledge about the quantitative recovery, e.g. in urine, of specific elements such as nitrogen or potassium, or stable isotopes administered to the study subjects (e.g. the doubly labelled water method for assessment of energy expenditure). This type of marker, however, eliminates the problem of correlation of random errors between Q and R by simply taking the place of R, thus rendering complex statistical models unnecessary.

Keywords

Food-frequency questionnairesValidation studiesStructural equation models
Type
Part H. Advances in the statistical evaluations and interpretation of dietary data
Information
Public Health Nutrition , Volume 5 , Issue 6a , December 2002 , pp. 969 - 976
Copyright
Copyright © CAB International 2002

References

1Kaaks, R, Riboli, E, van Staveren, W. Calibration of dietary intake measurements in prospective cohort studies. Am. J. Epidemiol. 1995; 142: 548–56.CrossRefGoogle ScholarPubMed
2Kaaks, R, Riboli, E. Validation and calibration of dietary intake measurements in the EPIC project: methodological considerations. European Prospective Investigation into Cancer and Nutrition. Int. J. Epidemiol. 1997; 26(Suppl. 1): S15–25.Google Scholar
3Freedman, LS, Carroll, RJ, Wax, Y. Estimating the relation between dietary intake obtained from a food frequency questionnaire and true average intake. Am. J. Epidemiol. 1991; 134: 310–20.Google Scholar
4Kaaks, R, Riboli, E, Esteve, J, van Kappel, AL, van Staveren, WA. Estimating the accuracy of dietary questionnaire assessments: validation in terms of structural equation models. Stat. Med. 1994; 13: 127–42.Google Scholar
5Plummer, M, Clayton, D. Measurement error in dietary assessment: an investigation using covariance structure models. Part I. Stat. Med. 1993; 12: 925–35.CrossRefGoogle ScholarPubMed
6Plummer, M, Clayton, D. Measurement error in dietary assessment: an investigation using covariance structure models. Part II. Stat. Med. 1993; 12: 937–48.CrossRefGoogle ScholarPubMed
7van Staveren, WA, Burema, J. Dietary methodology: implications of errors in the measurement. Proc. Nutr. Soc. 1990; 49: 281–7.Google Scholar
8Kipnis, V, Carroll, RJ, Freedman, LS, Li, L. Implications of a new dietary measurement error model for estimation of relative risk: application to four calibration studies. Am. J. Epidemiol. 1999; 150: 642–51.CrossRefGoogle ScholarPubMed
9Beaton, GH, Milner, J, Corey, P, McGuire, V, Cousins, M, Stewart, E, et al. Sources of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am. J. Clin. Nutr. 1979; 32: 2546–9.Google Scholar
10Nelson, M, Black, AE, Morris, JA, Cole, TJ. Between- and within-subject variation in nutrient intake from infancy to old age: estimating the number of days required to rank dietary intakes with desired precision. Am. J. Clin. Nutr. 1989; 50: 155–67.CrossRefGoogle ScholarPubMed
11Rosner, B, Willett, WC. Interval estimates for correlation coefficients corrected for within-person variation: implications for study design and hypothesis testing. Am. J. Epidemiol. 1988; 127: 377–86.Google Scholar
12Kaaks, RJ. Biochemical markers as additional measurements in studies of the accuracy of dietary questionnaire measurements: conceptual issues. Am. J. Clin. Nutr. 1997; 65: 1232S–9S.CrossRefGoogle ScholarPubMed
13Livingstone, MBE, Prentice, AM, Strain, JJ, Coward, WA, Black, AE, Barker, ME, et al. Accuracy of weighed dietary records in studies of diet and health. Br. Med. J. 1990; 300: 708–12.CrossRefGoogle ScholarPubMed
14Schoeller, DA. How accurate is self-reported dietary energy intake? Nutr. Rev. 1990; 48: 373–9.Google Scholar
15Bingham, SA. Limitations of the various methods for collecting dietary intake data. Ann. Nutr. Metab. 1991; 35: 117–27.CrossRefGoogle ScholarPubMed
16Spiegelman, D, Schneeweiss, S, McDermott, A. Measurement error correction for logistic regression models with an ‘alloyed gold standard’. Am. J. Epidemiol. 1997; 145: 184–96.Google Scholar
17Wong, MY, Day, NE, Bashir, SA, Duffy, SW. Measurement error in epidemiology: the design of validation studies I: univariate situation. Stat. Med. 1999; 18: 2815–29.3.0.CO;2-#>CrossRefGoogle ScholarPubMed
18Kaaks, R, Riboli, E, Sinha, R. Biochemical markers of dietary intake. IARC Sci. Publ. 1997; 103–26.Google ScholarPubMed
19Ferrari, P, Kaaks, R, Riboli, E. Variance and confidence limits in validation studies based on comparison between three different types of measurements. J. Epidemiol. Biostat. 2000; 5: 303–13.Google ScholarPubMed
20Bingham, SA, Gill, C, Welch, A, Cassidy, A, Runswick, SA, Oakes, S, et al. 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. 1997; 26(Suppl. 1): S137–51.Google Scholar
21Schoeller, DA. Measurement of energy expenditure in free-living humans by using doubly labeled water. J Nutr. 1988; 118: 1278–89.Google Scholar
22Willett, W, Stampfer, MJ. Total energy intake: implications for epidemiologic analyses. Am. J. Epidemiol. 1986; 124: 17–27.Google Scholar
23Kaaks, R, Riboli, ESlimani, N. Pilot-phase studies on the accuracy of dietary intake measurements in the EPIC project: overall summary and evaluation of results. Int. J. Epidemiol. 1997; 26: S26–36.Google Scholar