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Dietary measurement error has two consequences relevant to epidemiologic studies: first, a proportion of subjects are misclassified into the wrong groups, and second, the distribution of reported intakes is wider than the distribution of true intakes. While the first effect has been dealt with by several other authors, the second effect has not received as much attention. Using a simple errors-in-measurement model, the authors investigate the implications of measurement error for the distribution of fat intake. They then show how the inference of a more narrow distribution of true intakes affects the calculation of sample size for a cohort study. The authors give an example of the calculation for a cohort study investigating dietary fat and colorectal cancer. This shows that measurement error has a profound effect on sample size, requiring a six- to eightfold increase over the number required in the absence of error, if the correlation coefficient between reported and true intakes is 0.65. Reliable detection of a relative risk of 1.36 between a true intake of greater than 47.5% calories from fat and less than 25% calories from fat would require approximately one million subjects.
Freedman et al. (Sat,) studied this question.