New Approaches to Mediation Analysis using Causal Inference Methods

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DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (05) Comparative Effectiveness Research and specific Challenge Topics, 05-AA-101: Innovative Analysis of Existing Clinical Datasets and 05-AA-102: Adaptive Designs and Person-Centered Data Analysis for Alcohol Treatment Research. Abstract Mechanistic models of behavior change inherently describe causal processes; however, most analyses in the behavioral literature rely on the Baron and Kenny (1986) regression-based approach, which generally cannot be used to infer causal effects. Researchers may be reluctant to use causal models due to lack of full understanding of the models and lack of readily available software. This project is intended to bridge the gap between theory and practice of causal modeling for assessment of mediation in behavioral intervention studies, with specific focus on interventions for alcohol abuse. We use causal models of direct and indirect effects described by Robins and Greenland (1992) and Pearl (2001). The primary contributions of the proposed research will be development, application, evaluation and dissemination of statistical methods for fitting these models to observed data. The proposed research will develop statistical approaches for discovering pathways and mechanisms of behavioral interventions targeted at alcohol abuse. A major outcome of the research program is development, testing and dissemination of appropriate software for implementing the models. Knowledge of mechanistic pathways allows deeper understanding of how and why certain interventions work, and opens the door to customizing interventions based on person-specific characteristics.