Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events

2026-03-10

AI Summary

This study addresses the challenge that existing causal inference methods struggle to simultaneously handle semi-competing risks—such as death truncating the onset of hypertension—and time-dependent confounding in estimating time-varying treatment effects. The authors extend the g-computation framework to this complex setting, proposing two novel algorithms that model dynamic relationships among time-varying interventions, non-terminal events, and terminal events using Monte Carlo simulation and longitudinal cohort data. Simulation studies demonstrate that the proposed methods exhibit low bias and achieve nominal confidence interval coverage, outperforming current approaches. Applied to real-world data, the analysis reveals that sustained smoking cessation modestly reduces both midlife hypertension risk and mortality, thereby filling a critical methodological gap in epidemiological causal inference.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Background: A core aspect of epidemiology is determining the impacts of potential public health interventions over time. With long follow-up periods, epidemiologists may need to consider semi-competing events, in which a terminal event, like death, precludes a non-terminal event, like hypertension. Time-varying confounding poses an additional challenge when studying time-varying interventions or actions. Existing methods do not simultaneously address semi- competing events and time-varying confounding. Methods: We propose two novel g-computation algorithms for causal effects with semi- competing events and time-varying actions. To explore performance of our novel g-computation estimators, we conducted a Monte Carlo simulation study. We then applied our estimator to investigate how cigarette smoking prevention throughout young and middle adulthood might impact prevalent hypertension using data from Waves III (aged 18-26 years) - VI (aged 39-51 years) of the National Longitudinal Study of Adolescent to Adult Health. Results: Our simulations show that the novel g-computation estimators had little bias and appropriate confidence interval coverage. They outperformed existing alternative estimators across sample sizes. In the illustrative application, the novel estimator identified a small reduction in prevalence of hypertension and risk of death in midlife had all cigarette smoking been prevented across follow-up compared to the observed smoking patterns. Conclusion: As long-running cohorts progress in age, death within the study sample will become an increasing concern for studies of aging-related outcomes, life course analyses, and investigations into chronic disease development. Our novel g-computation estimators provide a simultaneous solution.

Problem

• semi-competing events
• time-varying confounding
• g-computation
• causal inference
• epidemiology

Innovation

• g-computation
• semi-competing events
• time-varying confounding
• causal inference
• longitudinal data