MNAR Mechanisms in Clinical Research

Music City Center 

In clinical settings, accurately weighing the probability of treatment benefits against the risks of side effects or adverse events is critical. However, inferring causal effects of treatments on multiple outcomes is often complicated by missing data on outcomes, which typically includes primary efficacy measures and secondary safety assessments. Building on foundational concepts that utilize graphical models to align missing data models with their causal counterparts, this talk focuses on an MNAR mechanism known as the block-conditional MNAR model. This model accounts for the influence of measured, unmeasured, or partially measured variables on the missingness indicators. We introduce novel nonparametric estimation methods designed to evaluate the trade-offs between population-averaged benefits and risks in clinical treatment decisions. These methods can accommodate mixed-type responses, such as binary and continuous data. The presentation will illustrate the methodological advancements and practical implications for robust inference on treatment effects in clinical settings.

MNAR

clinical research