Preventive Effect Heterogeneity: Causal Inference in Personalized Prevention.

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• Understanding variation in preventive effects is crucial for personalized interventions.
• Causal interaction and effect heterogeneity represent distinct forms of this variation.
• Existing methods require refinement for robust causal inference in personalized prevention.

Purpose of the Study:

• To differentiate and explore two forms of preventive effect heterogeneity: causal interaction and effect heterogeneity.
• To introduce and advocate for the baseline target moderated mediation (BTMM) design.
• To provide methods for developing personalized prevention targeting strategies using BTMM.

Main Methods:

• Employs a causal inference framework.
• Discusses causal interaction with manipulable moderators and effect heterogeneity with stable characteristics.
• Proposes the baseline target moderated mediation (BTMM) design for strengthened causal inference.

Main Results:

• Distinguishes between nonadditive effects from causal interaction and variation in causal structure from effect heterogeneity.
• Highlights BTMM as a promising design for personalized prevention.
• Reviews methods for addressing moderation confounding, including propensity score matching.

Conclusions:

• Personalized interventions can be advanced by understanding and leveraging different forms of preventive effect heterogeneity.
• The BTMM design offers a robust framework for causal inference and targeting in prevention.
• Further research and application of methods like propensity score matching are needed to minimize confounding in moderation analysis.