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24 h recall)

Figure 5 The relationship between test and reference measure and the truth. (Source: Nelson M (1997) The validation of dietary assessments. In: Margetts BM and Nelson M (eds.) Design Concepts in Nutritional Epidemiology, 2nd edn. Oxford: Oxford University Press.)

24 h recall)

Figure 5 The relationship between test and reference measure and the truth. (Source: Nelson M (1997) The validation of dietary assessments. In: Margetts BM and Nelson M (eds.) Design Concepts in Nutritional Epidemiology, 2nd edn. Oxford: Oxford University Press.)

likely under- or overreporters have been excluded) using the expression

where n is the number of repeat observations within one subject, and s2 and sb are the within- and between-subject variances, respectively. In this way, the likely relationship between the test measure and the truth can be estimated, and the relative risk can be adjusted to account for misclassification of subjects based on the test measure alone.

This approach has two weaknesses. First, if the reference measure is a dietary measure, it does not address the problem of correlation of errors (the tendency for an individual to misreport diet in the same way using the test and reference measures). If errors are correlated between methods, then the observed rrq is likely to overestimate rRT x tqt; it will appear that the test method is performing better than it actually is performing. If the errors are correlated within methods (e.g., if the same types of within-person bias are occurring from day to day using repeat 24-h recall), then the observed rrq is likely to underestimate rRT x rqt. The second weakness of this approach is that it does not address the problem of differential misclassification.

A similar technique is the method of triads (Figure 6), in which no assumption need be made about the relationships between reference measures and the truth. Instead, the relationships between three measures can be used to estimate values for p, which in theory approximate the correlation between each of the measures and the truth. As in the technique described previously, valid estimates of p are based on the assumption that the errors in the methods are uncorrelated and that the errors are random and not differentially biased between subjects.

Because the validation process helps to identify subjects who are likely to be misreporting their diet, the temptation may be to exclude from analysis those subjects who have misreported their diet. It may be, however, that the very subjects who are most likely to misreport their food consumption (e.g., people who are overweight) are also those who are at increased risk of disease (e.g., hypertension, heart disease, and colon cancer). In estimating disease risk, therefore, the aim must be to retain all of the subjects in the analysis.

Estimating the components of error and finding appropriate values for A is the best way to address this issue. A special case is to adjust nutrient intakes to allow for misreporting in some subjects by assuming that true energy intake and true nutrient intake in all subjects are well correlated. Thus, if a subject underreports energy intake, it is assumed that other nutrients will be underreported to a similar extent. By estimating nutrient intake in relation to reported energy intake, subjects can be ranked according to whether, for a given level of energy intake, their nutrient intake was above or below the average. This is known as energy adjustment. To find the energy-adjusted estimate of nutrient intake, the nutrient intakes should be plotted against energy intakes and the regression line and the residual values derived (Figure 7). Energy-adjusted nutrient intakes are then computed by adding the residual to the mean nutrient intake. This approach allows all subjects to be included in an analysis, and it provides realistic estimates of intake (unlike computations of nutrient density in which each subject's nutrient intake is divided by his or her energy intake). Like doubly labeled water, however, the

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