Potential Applications of Directed Acyclic Graphs in the Design and Interpretation of Biomedical Research

This article presents an algorithm for constructing and applying directed acyclic graphs (DAGs) in the planning of epidemiological and biomedical studies. DAGs are graphical tools for modeling complex relationships between variables, which is particularly relevant in biomedical science, where accurate assessment of causal relationships requires accounting for potential confounding factors. The importance of DAGs is emphasized for conceptualizing scientific hypotheses and understanding the structure of relationships between factors based on scientific data review and findings from previous studies. The use of DAGs enhances the quality of both study design and data analysis, providing a more grounded approach to selecting variables for inclusion in statistical models. DAGs make it possible to determine the minimal and sufficient set of factors for adjustment, with consideration of the roles of variables (confounders, mediators, colliders) in relation to the exposure (a probable risk factor) and the outcome (a disease or condition), thus reducing the likelihood of analytical errors. The article highlights the practical application of DAGs using available software and provides specific examples of their use in biomedical research. Finally, recommendations are offered for integrating DAGs into biomedical research practice, which may contribute to the broader adoption of modern multivariate statistical methods, improved interpretability, and enhanced reproducibility of scientific findings.