arXiv subjects

Yuexia Zhang

Publications and source records attributed to Yuexia Zhang.

8 recordsLinked to original sources

arXiv2024

Individualized Dynamic Mediation Analysis Using Latent Factor Models

Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes that mediation effects are static and homogeneous within populations. However, mediation effects usually change over time and exhibit significant heterogeneity among individuals in many real-world applications. Additionally, the mediation mechanism can be complicated and involves non-sparse, making mediator selection particularly challenging. To address these issues, we propose an individualized dynamic mediation analysis method for mediator selection. Our approach can identify the significant mediators at the population level while capturing the time-varying and heterogeneous mediation effects at the individual level via varying-coefficient structural equation models. Another advantage of our method is that we allow the presence of unmeasured time-varying confounders that induce the heterogeneous mediation effects. We provide asymptotic results for the proposed estimator and selection consistency for significant mediators. Extensive simulation studies and an application to a DNA methylation study demonstrate the effectiveness and advantages of our method.

stat.ME↗

arXiv2022

Weak signal identification and inference in penalized likelihood models for categorical responses

Penalized likelihood models are widely used to simultaneously select variables and estimate model parameters. However, the existence of weak signals can lead to inaccurate variable selection, biased parameter estimation, and invalid inference. Thus, identifying weak signals accurately and making valid inferences are crucial in penalized likelihood models. We develop a unified approach to identify weak signals and make inferences in penalized likelihood models, including the special case when the responses are categorical. To identify weak signals, we use the estimated selection probability of each covariate as a measure of the signal strength and formulate a signal identification criterion. To construct confidence intervals, we propose a two-step inference procedure. Extensive simulation studies show that the proposed procedure outperforms several existing methods. We illustrate the proposed method by applying it to the Practice Fusion diabetes data set.

stat.ME↗

arXiv2022

Empirical likelihood inference for longitudinal data with covariate measurement errors: An application to the LEAN study

Measurement errors usually arise during the longitudinal data collection process. Ignoring the effects of measurement errors will lead to invalid estimates. The Lifestyle Education for Activity and Nutrition (LEAN) study was designed to assess the effectiveness of intervention for enhancing weight loss over nine months. The covariates systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured at baseline, month 4, and month 9. At each assessment time, there were two replicate measurements for SBP and DBP. The replicate measurement errors of SBP follow different distributions, as does DBP. To account for the distributional difference of replicate measurement errors, a new method for analyzing longitudinal data with replicate covariate measurement errors is developed based on the empirical likelihood method. The asymptotic properties of the proposed estimator are established under some regularity conditions. The confidence region for the parameters of interest can be constructed based on the chi-squared approximation without estimating the covariance matrix. Additionally, the proposed empirical likelihood estimator is asymptotically more efficient than the estimator of Lin et al. (2018). Extensive simulations demonstrate that the proposed method can eliminate the effects of measurement errors in the covariate and has a high estimation efficiency. The proposed method indicates the significant effect of the intervention on BMI in the LEAN study.

stat.ME↗

arXiv2022

Estimation of local treatment effects under the binary instrumental variable model

Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper, we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges for causal estimation with a binary outcome, and show that surprisingly, it can be more difficult than the case with a continuous outcome. We propose novel modeling and estimating procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness or efficiency. Our approach is illustrated via simulation studies and a real data analysis.

stat.ME↗

arXiv2021

Inverse Probability Weighting-based Mediation Analysis for Microbiome Data

Mediation analysis is an important tool for studying causal associations in biomedical and other scientific areas and has recently gained attention in microbiome studies. Using a microbiome study of acute myeloid leukemia (AML) patients, we investigate whether the effect of induction chemotherapy intensity levels on infection status is mediated by microbial taxa abundance. The unique characteristics of the microbial mediators -- high-dimensionality, zero-inflation, and dependence -- call for new methodological developments in mediation analysis. The presence of an exposure-induced mediator-outcome confounder, antibiotic use, further requires a delicate treatment in the analysis. To address these unique challenges in our motivating AML microbiome study, we propose a novel nonparametric identification formula for the interventional indirect effect (IIE), a recently developed measure for assessing mediation effects. We develop a corresponding estimation algorithm using the inverse probability weighting method. We also test the presence of mediation effects via constructing the standard normal bootstrap confidence intervals. Simulation studies demonstrate that the proposed method has good finite-sample performance in terms of IIE estimation accuracy and the type-I error rate and power of the corresponding tests. In the AML microbiome study, our findings suggest that the effect of induction chemotherapy intensity levels on infection is mainly mediated by patients' gut microbiome.

stat.AP↗

arXiv2021

Robust Estimation of Propensity Score Weights via Subclassification

Weighting estimators based on propensity scores are widely used for causal estimation in a variety of contexts, such as observational studies, marginal structural models and interference. They enjoy appealing theoretical properties such as consistency and possible efficiency under correct model specification. However, this theoretical appeal may be diminished in practice by sensitivity to misspecification of the propensity score model. To improve on this, we borrow an idea from an alternative approach to causal effect estimation in observational studies, namely subclassification estimators. It is well known that compared to weighting estimators, subclassification methods are usually more robust to model misspecification. In this paper, we first discuss an intrinsic connection between the seemingly unrelated weighting and subclassification estimators, and then use this connection to construct robust propensity score weights via subclassification. We illustrate this idea by proposing so-called full-classification weights and accompanying estimators for causal effect estimation in observational studies. Our novel estimators are both consistent and robust to model misspecification, thereby combining the strengths of traditional weighting and subclassification estimators for causal effect estimation from observational studies. Numerical studies show that the proposed estimators perform favorably compared to existing methods.

stat.ME↗

arXiv2020

Conditional Independence Beyond Domain Separability: Discussion of Engelke and Hitz (2020)

We congratulate Engelke and Hitz on a thought-provoking paper on graphical models for extremes. A key contribution of the paper is the introduction of a novel definition of conditional independence for a multivariate Pareto distribution. Here, we outline a proposal for independence and conditional independence of general random variables whose support is a general set Omega in multidimensional real number space. Our proposal includes the authors' definition of conditional independence, and the analogous definition of independence as special cases. By making our proposal independent of the context of extreme value theory, we highlight the importance of the authors' contribution beyond this particular context.

math.ST↗

arXiv2013

Sequential Landau-Zener transitions in spin-orbit coupled systems

We investigate the Landau-Zener (LZ) process in spin-orbit coupled systems of single or multiple two-level (spin-$\frac{1}{2}$) particles. The coupling between internal spin states and external vibrational states, a simple spin-orbit coupling (SOC), is induced by applying a spin-dependent harmonic trap. Because of the SOC, the single-particle energy-level structures are modified by the Franck-Condon (FC) effects, in which some avoided energy-level-crossings (ELCs) are almost closed and some ELCs are opened. The close of avoided ELCs and the open of ELCs result in the FC blockade and the vibrational transitions, respectively. For a given low sweeping rate, the sequential LZ transitions of ladder-like population transition can be induced by strong SOC. We derive an analytical formula for the final population which is well consistent with the numerical results. For a given strong SOC, the sequential LZ transitions are submerged in the non-adiabatic transitions if the sweeping rate is sufficiently high. Further, we study LZ transitions of multiple interacting two-level Bose particles in a spin-dependent harmonic trap. The interplay between the SOC effects and the interaction effects is explored.

cond-mat.mes-hall↗