Bayesian Mediation Analysis for Causal Pathways

Music City Center 

Clinical trials are critical for determining the efficacy of medical interventions, yet the specific mechanisms by which these interventions produce their effects often remain unclear. This abstract introduces a Bayesian Mediation Analysis framework designed to shed light on the complex pathways through which treatment influences clinical outcomes. This approach:
1. Integrates Prior Knowledge.
Permits flexible modeling-both hierarchical and non-hierarchical-while incorporating informative priors, enhancing interpretability and robustness,
2. Naturally addresses Challenging Data Scenarios frequently encountered in clinical trials like: limited sample sizes, missing data, and nonlinear models,
3. Facilitates effect size comparisons.
Provides a systematic way to assess and compare direct versus indirect effects, and
4. Elucidates Causal Pathways by employing Gaussian Processes and Spline models
to capture heterogeneous underlying causal structures.
We demonstrate the method's utility through simulated datasets, showing how Bayesian Mediation Analysis can reveal nuanced treatment mechanisms and support more precise inferences about causal pathways.

Bayesian Mediation Analysis, Gaussian processes, Spline Models, Effect Size Comparisons, Stan, MCMCregress and brm R packages 

Biopharmaceutical Section