Practical Examples of Mediation Analyses for Clinical Trials using Both Frequentist and Bayesian App

Music City Center 

Mediation analysis aims to elucidate the intermediate variables or processes that mediate the relationship between an intervention and its ultimate impact on outcomes.This multifaceted approach enhances our understanding of not only whether an intervention works but also how it works, providing a comprehensive perspective for researchers, clinicians, and policymakers.By dissecting these pathways, researchers gain valuable insights into the causal chain of events, informing the development of more targeted and effective interventions.This presentation will cover both Bayesian informative and noninformative priors and frequentist approaches.Sizes of direct and indirect effects of treatment on dependent variable will be examined.Partial correlations between mediating paths will also be accounted for in modeling by including the correlation of their error term variances. Presenting a totality of evidence from using both approaches within a regulatory context can offer a more comprehensive and informed response, especially when navigating complex datasets and topics. We illustrate our findings by assessing the improvements in functioning scores in patients with MDD with simulations.

Mediation Analysis

Bayesian

Frequentist

Informative and non-informative priors 

Biopharmaceutical Section