A linear form relative risk model is used to identify circumstances in which various types of aggregate data lead to valid inferences on relative risk parameters. Upon making a random effects assumption, international or time trend data can lead to appropriate relative risk parameter estimation using iteratively reweighted least-squares procedures. Adequate confounding factor control, however, will typically require data on the distribution of confounding factors in each country or time period. For a simple interpretation of relative risk parameters one may also require data on the joint distribution of primary and confounding factors in each country or time period. Hence disease rate data need to be supplemented by dietary and risk factor survey data in order to avoid confounding bias. Measurement error in individual dietary assessment may, however, limit the ability to quantify the dependence of relative risk on dietary factors, unless the relative risk function is approximately linear in the dietary factors of interest. Most studies of migrant populations involve a comparison of migrant mortality rates with those of their countries of emigration and immigration, with little or no data collection on the dietary habits and risk factors of the migrants themselves. The potential of more comprehensive aggregate data and analytic migrant studies in the diet and disease area is briefly indicated. These issues and methods are illustrated using various types of data pertinent to the association between dietary fat and breast cancer.