Aplicación de grafos acíclicos dirigidos en la evaluación de un set mínimo de ajuste de confusores: un complemento al modelamiento estadístico en estudios epidemiológicos observacionales

Abstract

Directed acyclic graphs in statistical modelling of epidemiological studies 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-step backwards methodology was carried out. Results: The causal diagram included 12 non-causal open pathways. The minimum adjustment set corresponded to the variables "sex", "body mass index" and "fish and seafood ingest". Confusion retention of the multivariate model included normal and overweight status, gender and the interaction between "water intake" and GSTT1. Conclusions: The use of DAG prior to the modeling would allow a more comprehensive, coherent and biologically plausible analysis of causal relationships in public health.

Study protocol and rationale of “the UP project”: evaluating the effectiveness of active breaks on health indicators in desk-based workers

“…To determine which variables are appropriate to use as covariates, directed acyclic graphs will be explored. These graphs are built on the expert knowledge of the researcher, which would facilitate the causal understanding of the phenomenon and the type of linkage between the variables involved, minimizing the introduction of bias during the design of the study and in the analysis of results ( 80 , 81 ). All analyses will be performed in different software according to the needs of the researchers (e.g., R Studio, Jamovi, SPSS).…”

Physical activity level and factors associated with perceived stress among Peruvian university professors during the COVID-19 pandemic

“…The confounding variables were identifed using a directed acyclic graph [ [31] , [32] ]: Sex (male, female), head of household (yes, no), age, months in remote work, working hours per week (<30 h/week, ≥30 h/week), dedication (partial or full-time), and quality of sleep. This last measure determined by the Pittsburgh Sleep Quality Questionnaire, of Buysee [ 33 ], consists of 19 questions and the result is distributed into four categories.…”

Pre- and postnatal exposure to secondhand tobacco smoke and cardiometabolic risk at 12 years: Periods of susceptibility

“…We relied on previous literature and directed acyclic graphs (DAGs) ( Werlinger and Cáceres, 2018 ) to identify potential confounders for serum cotinine concentrations and adolescent’s CM risk ( Supplemental Figs. 1 and 2 ).…”