Directed Acyclic Graphs, Effect Measure Modification, and Generalizability.

Area of Science:

• Epidemiology
• Causal Inference
• Biostatistics

Background:

• Directed acyclic graphs (DAGs) are crucial in epidemiology for assessing internal validity.
• Their application to effect measure modification and external validity remains less explored.
• Understanding these aspects is vital for robust causal inference.

Purpose of the Study:

• To introduce two novel rules derived from DAGs for identifying effect measure modification.
• To demonstrate the utility of these DAG-based rules in generalizing findings from nested trials.

Main Methods:

• Development of two specific rules based on conditional independence and causal paths within DAGs.
• Application of Rule 1 to identify sufficient adjustment sets for generalization.
• Analysis of effect measure modification on various scales.

Main Results:

• Rule 1: Conditional independence of a variable P from outcome Y within treatment X levels implies P is not an effect measure modifier.
• Rule 2: Non-conditional independence coupled with open causal paths indicates P is an effect measure modifier.
• Rule 1 facilitates identification of adjustment sets for generalizing nested trial results.

Conclusions:

• DAGs provide a powerful framework for understanding effect measure modification.
• The proposed rules enhance the utility of DAGs for both internal and external validity in epidemiological studies.
• These methods improve the generalizability of causal effect estimates from trials.