Directed acyclic graphs and causal thinking in clinical risk prediction modeling.

ABSTRACT:

Background

In epidemiology, causal inference and prediction modeling methodologies have been historically distinct. Directed Acyclic Graphs (DAGs) are used to model a priori causal assumptions and inform variable selection strategies for causal questions. Although tools originally designed for prediction are finding applications in causal inference, the counterpart has remained largely unexplored. The aim of this theoretical and simulation-based study is to assess the potential benefit of using DAGs in clinical risk prediction modeling.

Methods

We explore how incorporating knowledge about the underlying causal structure can provide insights about the transportability of diagnostic clinical risk prediction models to different settings. We further probe whether causal knowledg

SUBMITTER: Piccininni M 

PROVIDER: S-EPMC7331263 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature