arXiv subjects

Xiaoran Liang

Publications and source records attributed to Xiaoran Liang.

4 recordsLinked to original sources

arXiv2026

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

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.

stat.ME↗

arXiv2024

Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables

We propose a procedure which combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous performance of the method in various settings. The method is applied to estimating the effect of immigration on wages.

stat.ME↗

arXiv2024

Instrumental Variable methods to target Hypothetical Estimands with longitudinal repeated measures data: Application to the STEP 1 trial

The STEP 1 randomized trial evaluated the effect of taking semaglutide vs placebo on body weight over a 68 week duration. As with any study evaluating an intervention delivered over a sustained period, non-adherence was observed. This was addressed in the original trial analysis within the Estimand Framework by viewing non-adherence as an intercurrent event. The primary analysis applied a treatment policy strategy which viewed it as an aspect of the treatment regimen, and thus made no adjustment for its presence. A supplementary analysis used a hypothetical strategy, targeting an estimand that would have been realised had all participants adhered, under the assumption that no post-baseline variables confounded adherence and change in body weight. In this paper we propose an alternative Instrumental Variable method to adjust for non-adherence which does not rely on the same `unconfoundedness' assumption and is less vulnerable to positivity violations (e.g., it can give valid results even under conditions where non-adherence is guaranteed). Unlike many previous Instrumental Variable approaches, it makes full use of the repeatedly measured outcome data, and allows for a time-varying effect of treatment adherence on a participant's weight. We show that it provides a natural vehicle for defining two distinct hypothetical estimands: the treatment effect if all participants would have adhered to semaglutide, and the treatment effect if all participants would have adhered to both semaglutide and placebo. When applied to the STEP 1 study, they both suggest a sustained, slowly decaying weight loss effect of semaglutide treatment.

stat.ME↗

arXiv2022

Selecting Valid Instrumental Variables in Linear Models with Multiple Exposure Variables: Adaptive Lasso and the Median-of-Medians Estimator

In a linear instrumental variables (IV) setting for estimating the causal effects of multiple confounded exposure/treatment variables on an outcome, we investigate the adaptive Lasso method for selecting valid instrumental variables from a set of available instruments that may contain invalid ones. An instrument is invalid if it fails the exclusion conditions and enters the model as an explanatory variable. We extend the results as developed in Windmeijer et al. (2019) for the single exposure model to the multiple exposures case. In particular we propose a median-of-medians estimator and show that the conditions on the minimum number of valid instruments under which this estimator is consistent for the causal effects are only moderately stronger than the simple majority rule that applies to the median estimator for the single exposure case. The adaptive Lasso method which uses the initial median-of-medians estimator for the penalty weights achieves consistent selection with oracle properties of the resulting IV estimator. This is confirmed by some Monte Carlo simulation results. We apply the method to estimate the causal effects of educational attainment and cognitive ability on body mass index (BMI) in a Mendelian Randomization setting.

stat.ME↗