Citation

Austin AE, Desrosiers TA, Shanahan ME. Child Abuse Negl. 2019; 91: 78-87.

Affiliation

Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599-7445, United States; Injury Prevention Research Center, University of North Carolina at Chapel Hill, 137 East Franklin Street, Suite 500, Chapel Hill, NC, 27599-7505, United States.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.chiabu.2019.02.011

PMID

30836237

Abstract

BACKGROUND: Child maltreatment research involves modeling complex relationships between multiple interrelated variables. Directed acyclic graphs (DAGs) are one tool child maltreatment researchers can use to think through relationships among the variables operative in a causal research question and to make decisions about the optimal analytic strategy to minimize potential sources of bias.

OBJECTIVE: The purpose of this paper is to highlight the utility of DAGs for child maltreatment research and to provide a practical resource to facilitate and support the use of DAGs in child maltreatment research.

RESULTS: We first provide an overview of DAG terminology and concepts relevant to child maltreatment research. We describe DAG construction and define specific types of variables within the context of DAGs including confounders, mediators, and colliders, detailing the manner in which each type of variable can be used to inform study design and analysis. We then describe four specific scenarios in which DAGs may yield valuable insights for child maltreatment research: (1) identifying covariates to include in multivariable models to adjust for confounding; (2) identifying unintended effects of adjusting for a mediator; (3) identifying unintended effects of adjusting for multiple types of maltreatment; and (4) identifying potential selection bias in data specific to children involved in the child welfare system.

CONCLUSIONS: Overall, DAGs have the potential to help strengthen and advance the child maltreatment research and practice agenda by increasing transparency about assumptions, illuminating potential sources of bias, and enhancing the interpretability of results for translation to evidence-based practice.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Causal diagrams; Colliders; Confounding; Directed acyclic graphs; Methodology; Selection bias