The effect of correlated measurement error in multivariate models of diet

Self-reported diet is prone to measurement error. Analytical models of diet may include several foods or nutrients to avoid confounding. Such multivariate models of diet may be affected by errors correlated among the dietary covariates, which may introduce bias of unpredictable direction and magnitude. The authors used 1993-1998 data from the European Prospective Investigation into Cancer and Nutrition in Norfolk, United Kingdom, to explore univariate and multivariate regression models relating nutrient intake estimated from a 7-day diet record or a food frequency questionnaire to plasma levels of vitamin C. The purpose was to provide an empirical examination of the effect of two different multivariate error structures in the assessment of dietary intake on multivariate regression models, in a situation where the underlying relation between the independent and dependent variables is approximately known. Emphasis was put on the control for confounding and the effect of different methods of controlling for estimated energy intake. The results for standard multivariate regression models were consistent with considerable correlated error, introducing spurious associations between some nutrients and the dependent variable and leading to instability of the parameter estimates if energy was included in the model. Energy adjustment using regression residuals or energy density models led to improved parameter stability.