Constructing causal pathways for premature cardiovascular disease mortality using directed acyclic graphs with integrating evidence synthesis and expert knowledge.

ABSTRACT: Cardiovascular disease (CVD) is a major global cause of premature mortality. While multiple studies propose CVD mortality prediction models based on regression frameworks, incorporating causal understanding through causal inference approaches can enhance accuracy. This paper demonstrates a methodology combining evidence synthesis and expert knowledge to construct a causal model for premature CVD mortality using Directed Acyclic Graphs (DAGs). The process involves three phases: (1) initial DAG development based on the Evidence Synthesis for Constructing Directed Acyclic Graphs (ESC-DAGs) framework, (2) validation and consensus-building with 12 experts using the Fuzzy Delphi method (FDM), and (3) application to data analysis using population-based survey data linked with death records. Exper

SUBMITTER: Hasani WSR 

PROVIDER: S-EPMC11582656 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature