Integrating prior knowledge into statistical models using directed acyclic graphs with application to the longitudinal association between life satisfaction and violent and aggressive behavior

Purpose: The process of designing, analyzing, and reporting a statistical investigation is dynamic, involving a trans-disciplinary team of experts. Frequently, expert knowledge is implicitly incorporated in the statistical design by a biostatistician and is unnoticed by other team members. This study articulates one tool that can move this integration process from an implicit to an explicit exercise in which all researchers can participate.

Methods: We use a study of the effect of life satisfaction (LS) on violent and aggressive behavior among children to illustrate how directed acyclic graphs (DAGs) can be used to explicitly incorporate expert knowledge into the design and analysis of complex data sets. DAGs are a mathematical tool used to identify structural relationships between independent and dependent variables. They can articulate confounding relationships, mediators and potential effect modifiers. The graph itself becomes a conceptual framework that guides the process of statistical model construction.

Results: We constructed a marginal structural model (MSM) predicated on the collaborative design of a DAG to study the effect of LS on violent and aggressive behavior. We were able to identify a subset of time-varying confounders that were explicitly incorporated into the MSM. We compared our results with an unadjusted regression and a confounder-adjusted regression model to illustrate the potential bias that can be introduced by using these traditional methods of identifying relationships between independent and dependent variables.

Conclusions: DAGs are a tool that can facilitate trans-disciplinary conversations related to statistical model construction.