  The construction of coherent prediction models holds great importance in
medical research as such models enable health researchers to gain deeper
insights into disease epidemiology and clinicians to identify patients at
higher risk of adverse outcomes. One commonly employed approach to developing
prediction models is variable selection through penalized regression
techniques. Integrating natural variable structures into this process not only
enhances model interpretability but can also %increase the likelihood of
recovering the true underlying model and boost prediction accuracy. However, a
challenge lies in determining how to effectively integrate potentially complex
selection dependencies into the penalized regression. In this work, we
demonstrate how to represent selection dependencies mathematically, provide
algorithms for deriving the complete set of potential models, and offer a
structured approach for integrating complex rules into variable selection
through the latent overlapping group Lasso. To illustrate our methodology, we
applied these techniques to construct a coherent prediction model for major
bleeding in hypertensive patients recently hospitalized for atrial fibrillation
and subsequently prescribed oral anticoagulants. In this application, we
account for a proxy of anticoagulant adherence and its interaction with dosage
and the type of oral anticoagulants in addition to drug-drug interactions.
