Causal framework for analyzing mediation effects of clinical biomarkers

Music City Center 

For a biomarker to be at least a "level 3 surrogate" that is "reasonably likely to predict clinical benefit for a specific disease and class of interventions" it must be either a mediator on the causal pathway between treatment and response, or else be causally downstream of such a mediator. We investigate causal mediation analysis as an approach to statistically infer potential mediation effects of biomarkers. Steps involve graphically stating the causal structure using DAGs, formulating estimands of interest and using statistical methods to derive estimates. However, longitudinal clinical data are commonplace and causal estimation of such data is notoriously challenging, standard statistical methods might not provide appropriate target estimates. Thus, we also explore methods to account for time-varying confounding in mediation analysis, one such method discussed provides a reasonable approximation by "Landmarking" the biomarker process at a particular timepoint t, and modeling the clinical outcome data after time t. We aim to outline fundamental ideas of causal mediation analysis and delineate a potential framework for its use in clinical development.

biomarkers

causal inference

mediation

estimands 

Biopharmaceutical Section