11: Stable Balancing Weights with Extreme Value Adjustment in Longitudinal Studies

Thomas M. Menino Convention & Exhibition Center 

Longitudinal observational studies are increasingly used to evaluate treatment effectiveness in real-world settings, yet valid causal inference remains challenging when covariates evolve over time. Treatment groups in longitudinal studies may diverge over time due to differences in treatment intensity, adherence, disease progression, or behavioral changes, resulting in longitudinal trajectory divergence that can increase bias and variance in treatment effect estimation. In this paper, we focus on a hybrid design representing a naturalistic follow-up study after initial randomization, with fixed treatment and time-varying outcomes and covariates. We propose an extreme value theory-based stable balancing weights method (EVT-SBW) that directly targets covariate balance through convex optimization and penalizes individual weights using EVT when overlap is limited due to extreme trajectory imbalance. Unlike ad hoc trimming or approaches that redefine the target estimand, EVT-SBW aims to improve efficiency and stability without discarding observations. Simulation studies demonstrate that EVT-SBW reduces bias and RMSE across a wide range of longitudinal scenarios with limited overlap.

longitudinal studies

convex optimization

trajectory imbalance

extreme value theory

limited overlap 

Biopharmaceutical Section