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Public Health Nutr. 2002 Dec;5(6A):969-76.

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

Author information

  • 1International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon Cedex 08, France. kaaks@iarc.fr

Abstract

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 formalized 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.

PMID:
12638598
[PubMed - indexed for MEDLINE]
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