Implications of a new dietary measurement error model for estimation of relative risk: application to four calibration studies.

Abstract

Food records or 24-hour recalls are currently used to calibrate food frequency questionnaires (FFQs) and to correct disease risks for measurement error. The standard regression calibration approach requires that these reference measures contain only random within-person errors uncorrelated with errors in FFQs. Increasing evidence suggests that records/recalls are likely to be also flawed with systematic person-specific biases, so that for any individual the average of multiple replicate assessments may not converge to her/his true usual nutrient intake. The authors propose a new measurement error model to accommodate person-specific bias in the reference measure and its correlation with systematic error in the FFQ. Sensitivity analysis using calibration data from four studies demonstrates that failure to account for person-specific bias in the reference measure can often lead to substantial underestimation of the relative risk for a nutrient. These results indicate that in the absence of information on the extent of person-specific biases in reference instruments and their relation to biases in FFQs, the adequacy of the standard methods of correcting relative risks for measurement error is in question, as is the interpretation of negative findings from nutritional epidemiology such as failure to detect an important relation between fat intake and breast cancer.