Presented by Fan Li
Associate Professor of Biostatistics
Yale School of Public Health

Intercurrent events, common in clinical trials, affect the existence or interpretation of final outcomes. Principal stratification addresses these challenges by defining local average treatment effects within latent subpopulations but often relies on restrictive assumptions such as monotonicity and/or counterfactual intermediate independence. To address these limitations, we propose a unified semiparametric framework for principal stratification analysis leveraging a margin-free, conditional odds ratio sensitivity parameter. Under partial principal ignorability, we derive nonparametric identification formulas and develop efficient estimation methods, including a conditionally doubly robust parametric estimator and a de-biased machine learning estimator with data-adaptive nuisance estimators. Simulations show that incorrectly assuming monotonicity can often lead to suboptimal inference, while specifying non-trivial odds ratio sensitivity parameter can enable approximately valid inference under monotonicity. We apply our methods to a critical care clinical trial to gain further insights.

A seminar tea will be held at 2:45 p.m. in University Office Plaza, Room 240.

All are Welcome.