Interpretation of Epidemiological Data Using Multiple Correspondence Analysis and Log-linear Models

Abstract

In this work we present a combined approach to contingency tables analysis using correspondence analysis and log-linear models. Several investigators have recognized relations between the aforementioned method-ologies, in the past. By their combination we may obtain a better under-standing of the structure of the data and a more favorable interpretation of the results. As an application we applied both methodologies to an epi-demiological database (CARDIO2000) regarding coronary heart disease risk factors.