Visual Stratification for Epidemiological Analysis

Abstract

Investigating etiology of a disease depends on the combination of tacit medical knowledge and multivariate analysis on a wide array of collected data. Confounding variables may generate a bias when exploring disease determinants, thus, reducing the predictive capabilities of risk factors. Stratified analysis is widely used in epidemiological settings to reduce the effect of confounding factors. We propose a stratified visual analysis approach based on linear projections and interactions in a Star Coordinates Plot (SCP), where the segregation power of dimensions in multiple strata can be explored interactively. We apply our approach to gain insight into three epidemiological results using stratified analysis regarding the prevalence of sleep apnea within age and gender strata and the segregating power of well-defined epidemiological risk factors.