Directed acyclic graphs: An under-utilized tool for child maltreatment research |
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Institution: | 1. 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;2. Injury Prevention Research Center, University of North Carolina at Chapel Hill, 137 East Franklin Street, Suite 500, Chapel Hill, NC, 27599-7505, United States;3. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599-7445, United States;1. Department of Orthodontics, School of Dental Medicine, State University of New York, Buffalo, NY;2. Associate Editor of Statistics and Research Design; Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine/Medical Faculty, University of Bern, Bern, Switzerland; private practice, Corfu, Greece;3. Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan;1. Unit of Colorectal Surgery, Department of Medical, Surgical, Neurologic, Metabolic and Ageing Sciences, Second University of Naples, Piazza Miraglia 2, 80138 Naples, Italy;2. Department of The Woman, The Child and General and Specialized Surgery, Second University of Naples, Piazza Miraglia 2, 80138 Naples, Italy;3. Department of Internal and Experimental Medicine, Magrassi-Lanzara, Institute of Radiology, Second University of Naples, Piazza Miraglia 2, 80138 Naples, Italy;4. Unit of General and Geriatric Surgery, Department of Medical, Surgical, Neurologic, Metabolic and Ageing Sciences, Second University of Naples, Piazza Miraglia 2, 80138 Naples, Italy;1. Department of Anesthesiology, School of Medicine, University of North Carolina at Chapel Hill, United States;2. Department of Psychology, University of Nevada, Las Vegas, United States;3. Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, United States;4. Injury Prevention Research Center, University of North Carolina at Chapel Hill, United States;5. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, United States |
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Abstract: | BackgroundChild 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.ObjectiveThe 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.ResultsWe 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.ConclusionsOverall, 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. |
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Keywords: | Directed acyclic graphs Causal diagrams Confounding Selection bias Colliders Methodology |
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