🤖 AI Summary
This study addresses the risk of introducing non-causal features and collider bias when constructing molecular signatures of lifestyle exposures without accounting for underlying causal structures. Leveraging directed acyclic graphs (DAGs) and d-separation theory, the work systematically elucidates, for the first time, the causal implications of univariate screening in feature construction and proposes incorporating this step prior to multivariable modeling to mitigate bias. Simulation studies demonstrate that while this strategy slightly reduces sensitivity and the correlation between exposure and signature, it substantially decreases the inclusion of non-causal features, yielding a feature set more aligned with the underlying causal mechanisms. Consequently, the approach offers clear advantages for mechanistic investigations seeking biologically interpretable signatures.
📝 Abstract
Molecular signatures derived from omics data are increasingly used in epidemiological studies to characterize lifestyle exposures, either as proxies of exposure or to provide insight into disease mechanisms. These signatures are typically constructed by regressing the exposure on high-dimensional omics features. In the literature, an initial univariate screening step has sometimes been applied prior to multivariate modelling, but the causal implications of this choice have not yet been considered. Focusing on settings where the exposure causally influences molecular features (and not the reverse), we use directed acyclic graphs (DAGs) and $d$-separation arguments to show that collider bias may arise when the screening step is ignored, leading to the inclusion of non-causal features in the signature. We further demonstrate that the screening step can mitigate this bias. Our simulation studies illustrate that screening reduces the inclusion of non-causal features, albeit at the cost of lower sensitivity and reduced correlation between the exposure and the resulting signature. Overall, we recommend applying univariate screening prior to signature construction, particularly when the inclusion of non-causal features is undesirable, such as in mechanistic studies.