Within- and between-cohort variation in measured macronutrient intakes, taking account of measurement errors, in the European Prospective Investigation into Cancer and Nutrition study.
American Journal of Epidemiology 2004 ; 160: 814-22.
Ferrari P, Kaaks R, Fahey MT, Slimani N, Day NE, Pera G, Boshuizen HC, Roddam A, Boeing H, Nagel G, Thiebaut A, Orfanos P, Krogh V, Braaten T, Riboli E, European Prospective Investigation into Cancer and Nutrition study
DOI : 10.1093/aje/kwh280
PubMed ID : 15466504
Multicenter epidemiologic studies provide a unique opportunity to evaluate the association between exposure and disease at the individual and the aggregate levels. The two components can eventually be pooled to corroborate each other, using weights proportional to the intraclass correlation coefficient (ICC), which expresses the amount of between-cohort variability in the exposure variable compared with the total. The greater the ICC, the more the overall estimate will reflect the between-cohort component. Dietary measurements are affected by measurement errors, particularly within a cohort. In 1992-2000, the variability of macronutrient intake distribution before and after calibration for measurement error in the European Prospective Investigation into Cancer and Nutrition was evaluated. A two-level, random-effects model was used. Evaluation of macronutrient densities revealed that energy has a considerable effect on the calibration model, leading to ICC values larger than those for the absolute intakes. Given the shrinkage of the within-center variability, a sizable increase in the ICC was observed for protein in men and women (0.48 and 0.54, respectively) and carbohydrates in men (0.41). Results suggest that the effect of calibration on macronutrient intake variability is greater for the within-cohort component, thus increasing the relative importance of the between-cohort component. After calibration, the two components had a similar weight. This observation has important implications for the analysis of multicenter studies because the between-cohort component provides a large part of the overall heterogeneity.