Impact of Exposure Measurement Error in Nutritional Epidemiology

Abstract

For the past several decades, observational studies of diet and cancer have yielded many inconsistent results ( 1 ). Given the limited variation in dietary intakes within many study populations and the seemingly modest diet – cancer associations, results of such studies depend critically on an accurate assessment of the dietary exposure ( 2 ). Measurement error in exposure leads to seriously biased relative risks of cancer for dietary intakes and substantially reduces the statistical power to detect existing relationships ( 2 ). The international consortium of cohort studies known as the Pooling Project ( 3 ) aims to gain precision in relative risk estimates and overcome this loss of statistical power by combining analyses of individual data from multiple studies that examine associations between diet and cancer. In this issue of the Journal, Lee et al. ( 4 ) report the results of an analysis of fat, protein, and meat consumption in relation to the risk of renal cell cancer in 13 cohorts within the Pooling Project. Using multivariable models, they obtained null results for all of the associations they considered. In this editorial, we comment on the impact of exposure measurement error on the estimated associations between diet and disease in this and other studies. Exposure measurement error in prospective studies can usually be assumed to be nondifferential with respect to disease, that is, the relationship between the reported diet and the true diet is the same for the case subjects and the control subjects. When the exposure, but none of the confounders, is measured with error, this error attenuates the relative risk (ie, biases the relative risk toward no effect). Although statistical tests for the association remain valid, that is, they preserve the nominal statistical signifi cance level ( 5 ), the measurement error reduces the statistical power to detect the association. Consequently, the number of cases required to maintain the nominal statistical power is increased by a factor equal to the inverse-squared correlation between the reported exposure and the true exposure ( 6 ). Moreover, although adjustment for measurement error can remove existing bias in estimated relative risks, it cannot compensate for the loss of statistical power. Within the Pooling Project, the main dietary assessment instruments were study-specifi c food-frequency questionnaires (FFQs) and closely related diet histories. Most studies also included validation subsamples with multiple food records or 24-hour