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The use and interpretation of energy-adjustment regression models in nutritional epidemiology has been vigorously debated recently. There has been little discussion, however, regarding the effect of dietary measurement error on the performance of such models. Contrary to conventional assumptions invoked in the standard treatment of the effect of measurement error in regression analysis, reporting errors in dietary studies are usually biased, correlated with true nutrient intakes and with each other, heteroscedastic, and nonnormally distributed. Methods developed in this paper allow for this more complex error structure and are therefore more appropriate for dietary data. For practical illustration, these methods are applied to data from the Women's Health Trial Vanguard Study. The results demonstrate considerable shrinkage in the magnitude of the estimated main exposure effect in energy-adjustment models due to attenuation of the true effect and contamination from the effect of an adjusting covariate. In most cases, this shrinkage causes a sharply reduced statistical power of the corresponding significance test in comparison with measurement without error. These results emphasize the need to understand the measurement error properties of dietary instruments through validation/calibration studies and, where possible, to correct for the impact of measurement error when applying energy-adjustment models.
Kipnis et al. (Sat,) studied this question.