Directed acyclic graphs in statistical modelling of epidemiological studies Aplicación de grafos acíclicos dirigidos en la evaluación de un set mínimo de ajuste de confusores: Un complemento al modelamiento estadístico en estudios epidemiológicos observac

Abstract

© 2018, Sociedad Medica de Santiago. All rights reserved. Background: Confusion in observational epidemiological studies distorts the relationship between exposure and event. “Step by step” regression models, diverts the decision to a statistical algorithm with little causal basis. Directed Acyclic Graphs (DAGs), qualitatively and visually assess the confusion. They can complement the decision on confounder control during statistical modeling. Aim: To evaluate the minimum set of confounders to be controlled in a cause-effect relationship with the use of “step-by-step regression” and DAGs, in a study of arsenic exposure. Material and Methods: We worked with data from Cáceres et al., 2010 in 66 individuals from northern Chile. The interindividual variability in the urinary excretion of dimethyl arsenic acid attributable to the GSTT1 polymorphism was estimated. A causal DAG was constructed using DAGitty v2.3 with the list of variables. A multiple linear regression model with the step-by-s