Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death

2025-09-17

AI Summary

This study addresses key challenges in electronic health record (EHR) data: dynamically evolving treatment strategies over time, non-monotonic repeated outcome measurements, sparsity, informative missingness, and death truncation—where outcomes become undefined post-death. To robustly estimate causal effects of generalized time-varying treatment regimes on repeated outcomes, we propose a novel estimator—“temporally smoothed inverse probability weighting” (TS-IPW)—that integrates temporal smoothing into the IPW framework. Simulation studies demonstrate substantial improvements in estimation accuracy and stability compared to conventional IPW. Empirical analysis of real-world EHR data reveals heterogeneous causal effects of alternative antidepressant prescribing strategies on longitudinal body weight trajectories. To facilitate reproducible causal inference in clinical settings, we release an open-source R package, *smoothedIPW*, implementing the proposed methodology.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Researchers are often interested in estimating effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. For example, the Medications and Weight Gain in PCORnet (MedWeight) study aimed to estimate effects of adhering to flexible medication regimes on future weight change using electronic health records (EHR) data. This problem presents several methodological challenges that have not been jointly addressed in the prior literature. First, this setting involves treatment strategies that vary over time and depend dynamically and non-deterministically on measured confounder history. Second, the outcome is repeatedly, non-monotonically, informatively, and sparsely measured in the data source. Third, some individuals die during follow-up, rendering the outcome of interest undefined at the follow-up time of interest. In this article, we pose a range of inverse probability weighted (IPW) estimators targeting effects of generalized time-varying treatment strategies in truncation by death settings that allow time-smoothing for precision gain. We conducted simulation studies that confirm precision gains of the time-smoothed IPW approaches over more conventional IPW approaches that do not leverage the repeated outcome measurements. We illustrate an application of the IPW approaches to estimate comparative effects of adhering to flexible antidepressant medication strategies on future weight change. The methods are implemented in the accompanying R package, smoothedIPW.

Problem

• Estimating effects of time-varying treatment strategies on outcomes
• Addressing non-monotonic and sparse repeated outcome measurements
• Handling outcome truncation due to death during follow-up

Innovation

• Time-smoothed inverse probability weighted estimators
• Address truncation by death with repeated outcomes
• Handle dynamic non-deterministic treatment strategies