We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
More (Adjustment) Is Not Always Better: How Directed Acyclic Graphs Can Help Researchers Decide Which Covariates to Include in Models for the Causal Relationship between an Exposure and an Outcome in Observational Research.
- Authors
Diemer, Elizabeth W.; Hudson, James I.; Javaras, Kristin N.; Diemer, Elizabeth W; Hudson, James I; Javaras, Kristin N
- Abstract
Causal DAGs can clarify how adjustment for a given covariate might impact bias, by providing a simple way to visualize assumptions about the statistical relationships between the exposure, outcome, and covariates in question. Keywords: Causal inference; Bias; Directed acyclic graph; DAG; Methods EN Causal inference Bias Directed acyclic graph DAG Methods 289 298 10 08/07/21 20210901 NES 210901 Introduction When constructing a model for an outcome of interest (e.g., a linear regression model), the choice of covariates to be included depends in part on the researcher's aims. Depending on which of these paths is present, including maternal smoking as a covariate (i.e., in the causal model for the effect of preterm birth on ADHD) could increase bias, reduce bias, or both.
- Subjects
CAUSAL models; LOW birth weight
- Publication
Psychotherapy & Psychosomatics, 2021, Vol 90, Issue 5, p289
- ISSN
0033-3190
- Publication type
editorial
- DOI
10.1159/000517104