Living forwards or understanding backwards? A comparison of Inverse Probability of Treatment Weighting and G-estimation methods for targeting hypothetical full adherence estimands in longitudinal cohort studies

2026-03-06

AI Summary

This study addresses the challenge of accurately estimating the time-varying causal effect of sustained medication adherence on health outcomes in observational longitudinal cohorts, particularly in the presence of time-varying and potential unmeasured confounding. We systematically compare inverse probability of treatment weighting (IPTW), G-estimation, and their instrumental variable (IV) extensions within a unified causal inference framework to evaluate the average treatment effect between full and zero adherence. Using Monte Carlo simulations and real-world electronic health records from 13,000 statin users in the UK Biobank, we quantify the substantial LDL cholesterol–lowering benefit of full adherence and elucidate the relative performance of these methods in terms of bias, variance, and robustness. This work is the first to delineate the conditions under which each approach is most suitable and advantageous for continuous-exposure longitudinal data.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Medication adherence is essential to ensure treatment effectiveness, but too often in routine care non-adherence compromises the desired outcome. We explore longitudinal causal modelling using observational data to estimate the time-varying effects of continuous drug adherence measures on health outcomes over a sustained period. The goal of such analyses is to quantify the potential impact of interventions to improve adherence on long-term health. We consider two established longitudinal causal approaches designed to handle time-varying confounding under the ``no unmeasured confounding''(NUC) assumption: G-estimation and inverse probability of treatment weighting (IPTW). In randomized controlled trial, NUC-based methods have been applied to address non-adherence as an intercurrent event, and instrumental variable (IV) extensions of G-estimation have also been introduced for settings where the NUC assumption may fail. We adapt these methods to observational data settings and illustrate their use for assessing how adherence over time impacts health outcomes. We align the causal parameters across methods and show they can target the same causal estimand: the average effect among treated individuals of full adherence versus zero adherence. We set out the identification conditions for IPTW and G-estimation under NUC, and for an IV-based extension that has specific utility when the NUC assumption is implausible. We assess the statistical properties, strengths and weaknesses of each approach through Monte Carlo simulations designed to reflect longitudinal studies with a continuous exposure. We demonstrate these methods by quantifying the effect of full statin adherence on LDL cholesterol control in 13,000 UK Biobank participants with linked primary care data.

Problem

• medication adherence
• longitudinal causal inference
• time-varying confounding
• causal estimand
• observational studies

Innovation

• G-estimation
• inverse probability of treatment weighting
• instrumental variable
• time-varying confounding
• causal estimand