Evaluating the impact of longitudinal treatment strategies in the presence of informative monitoring and time-dependent confounding

2026-04-10

AI Summary

This study addresses the challenge of estimating the causal effect of longitudinal treatment strategies on survival outcomes using electronic health records, where monitoring frequency of covariates varies across patients, variable types, and time, and may itself carry information about underlying health status—potentially biasing conventional causal inference methods. For the first time, monitoring indicators are formally treated as time-varying confounders. The authors integrate inverse probability weighting, G-computation, and longitudinal targeted maximum likelihood estimation (TMLE) to develop a unified framework for causal effect estimation under informative monitoring. This approach substantially reduces bias arising from ignoring the monitoring mechanism and extends the applicability of both static and dynamic treatment strategies. Simulations confirm its validity, and an application to real-world ICU data demonstrates its ability to accurately assess the impact of different mechanical ventilation initiation strategies on mortality.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Multiagent Systems: Mechanism Design

Application Category

• 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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Routinely collected data from electronic health records (EHR) provide opportunities to study effects of longitudinal treatment strategies in real-world clinical settings. A challenge presented by EHR data is that frequency of covariate monitoring differs by patient, covariate type and over time, and may be informative about a patient's health status. Many causal inference methods assume measurements of covariates are observed at a common set of regular time points. In this paper we describe and evaluate methods for estimating causal effects of longitudinal treatments on time-to-event outcomes in the presence of informative monitoring of time-dependent confounders. We show how methods based on inverse probability weighting, G-computation and longitudinal targeted maximum likelihood estimation (TMLE) can be adapted to allow for informative monitoring by incorporating monitoring indicator variables as additional time-dependent confounders. We evaluate these methods using a simulation study, comparing against more simple approaches that ignore monitoring variables. We demonstrate that ignoring monitoring can result in biased estimates of treatment effects. The methods are illustrated through an investigation into the effect of early versus delayed initiation of invasive mechanical ventilation on mortality of intensive care patients using routinely-collected data from an intensive care unit. We consider static treatment strategies such as `always treat'and `never treat'but also generalise to treatment strategies that allow for flexibility in the exact initiation time and duration of treatment.

Problem

• informative monitoring
• time-dependent confounding
• longitudinal treatment strategies
• causal inference
• electronic health records

Innovation

• informative monitoring
• time-dependent confounding
• longitudinal causal inference
• targeted maximum likelihood estimation
• electronic health records