Publications by authors named "Sarah C Gadd"
Background: Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research.
Methods: Original health research articles published during 1999-2017 mentioning 'directed acyclic graphs' (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase.
Article Synopsis
- Longitudinal data analysis is essential for informing disease prevention policies by examining how variables change over time.
- Different analytical methods treat this data either as discrete measurements or continuous patterns, impacting the interpretation of causal relationships.
- Simulations showed that methods conditioning on the outcome can lead to misleading conclusions about causal effects, emphasizing the need for careful approach selection in longitudinal studies.