arXiv subjects

Mengqi Xu

Publications and source records attributed to Mengqi Xu.

6 recordsLinked to original sources

arXiv2026

Robust inference for risk heterogeneity under group imbalance

Population-level heterogeneity is ubiquitous in biomedical data, where differences across demographic or clinical subgroups can substantially alter risk patterns. For example, in intensive care unit (ICU) studies, the mortality risk associated with specific admission diagnoses can vary across ethnic groups. Existing approaches for detecting risk heterogeneity are often sensitive to baseline model misspecification and regularization bias, both of which commonly arise in practice. In this paper, we propose a robust framework for inferring risk heterogeneity between two populations using Neyman orthogonality, which yields estimators that are locally insensitive to nuisance parameter estimation error. The proposed estimator is consistent and asymptotically normal, and simulation studies demonstrate that in finite samples our method substantially reduces bias and improves inferential stability compared with standard likelihood-based approaches. In an application to the eICU Collaborative Research Database, our method reveals clinically meaningful ethnicity-specific heterogeneity in admission diagnoses for in-hospital mortality that standard likelihood-based methods fail to detect.

stat.ME↗

arXiv2025

Diagnosis-based mortality prediction for intensive care unit patients via transfer learning

In the intensive care unit, the underlying causes of critical illness vary substantially across diagnoses, yet prediction models accounting for diagnostic heterogeneity have not been systematically studied. To address the gap, we evaluate transfer learning approaches for diagnosis-specific mortality prediction and apply both GLM- and XGBoost-based models to the eICU Collaborative Research Database. Our results demonstrate that transfer learning consistently outperforms models trained only on diagnosis-specific data and those using a well-known ICU severity-of-illness score, i.e., APACHE IVa, alone, while also achieving better calibration than models trained on the pooled data. Our findings also suggest that the Youden cutoff is a more appropriate decision threshold than the conventional 0.5 for binary outcomes, and that transfer learning maintains consistently high predictive performance across various cutoff criteria.

cs.LG↗

arXiv2025

Enhancing Inference for Small Cohorts via Transfer Learning and Weighted Integration of Multiple Datasets

Lung sepsis remains a significant concern in the Northeastern U.S., yet the national eICU Collaborative Database includes only a small number of patients from this region, highlighting underrepresentation. Understanding clinical variables such as FiO2, creatinine, platelets, and lactate, which reflect oxygenation, kidney function, coagulation, and metabolism, is crucial because these markers influence sepsis outcomes and may vary by sex. Transfer learning helps address small sample sizes by borrowing information from larger datasets, although differences in covariates and outcome-generating mechanisms between the target and external cohorts can complicate the process. We propose a novel weighting method, TRANSfer LeArning wiTh wEights (TRANSLATE), to integrate data from various sources by incorporating domain-specific characteristics through learned weights that align external data with the target cohort. These weights adjust for cohort differences, are proportional to each cohort's effective sample size, and downweight dissimilar cohorts. TRANSLATE offers theoretical guarantees for improved precision and applies to a wide range of estimands, including means, variances, and distribution functions. Simulations and a real-data application to sepsis outcomes in the Northeast cohort, using a much larger sample from other U.S. regions, show that the method enhances inference while accounting for regional heterogeneity.

stat.ME↗

arXiv2025

The R Package WMAP: Tools for Causal Meta-Analysis by Integrating Multiple Observational Studies

Integrating multiple observational studies for meta-analysis has sparked much interest. The presented R package WMAP (Weighted Meta-Analysis with Pseudo-Population) addresses a critical gap in the implementation of integrative weighting approaches for multiple observational studies and causal inferences about various groups of subjects, such as disease subtypes. The package features three weighting approaches, each representing a special case of the unified weighting framework introduced by Guha and Li (2024), which includes an extension of inverse probability weights for data integration settings. It performs meta-analysis on user-inputted datasets as follows: (i) it first estimates the propensity scores for study-group combinations, calculates subject balancing weights, and determines the effective sample size (ESS) for a user-specified weighting method; and (ii) it then estimates various features of multiple counterfactual group outcomes, such as group medians and differences in group means for the mRNA expression of eight genes. Additionally, bootstrap variability estimates are provided. Among the implemented weighting methods, we highlight the FLEXible, Optimized, and Realistic (FLEXOR) method, which is specifically designed to maximize the ESS within the unified framework. The use of the software is illustrated by simulations as well as a multi-site breast cancer study conducted in seven medical centers.

stat.ME↗

arXiv2020

Comparison of different approaches to single-molecule imaging of enhanced enzyme diffusion

Enzymes have been shown to diffuse faster in the presence of their reactants. Recently, we revealed new insights into this process of enhanced diffusion using single-particle tracking (SPT) with total internal reflection fluorescence (TIRF) microscopy. We found that the mobility of individual enzymes was enhanced three fold in the presence of the substrate, and the motion remained Brownian. In this work, we compare different experimental designs, as well as different data analysis approaches, for studying single enzyme diffusion. We first tether enzymes directly on supported lipid bilayers (SLBs) to constrain the diffusion of enzymes to two dimensions. This experimental design recovers the 3-fold enhancement in enzyme diffusion in the presence of the substrate, as we observed before. We also simplify our system by replacing the bulky polymers used in the prior chamber design with a SLB-coated surface and glycerol. Using this newly-designed SLB/glycerol chamber, we compare two different analysis approaches for SPT: the mean-squared displacement (MSD) analysis and the jump-length analysis. We find that the MSD analysis requires high viscosity and large particles to accurately report the diffusion coefficient, while jump-length analysis depends less on the viscosity or size. Furthermore, the SLB-glycerol chamber fails to reproduce the enhanced diffusion of enzymes because glycerol inhibits enzyme activity.

physics.bio-ph↗

arXiv2018

Direct Single Molecule Imaging of Enhanced Enzyme Diffusion

Recent experimental results have shown that active enzymes can diffuse faster when they are in the presence of their substrates. Fluorescence correlation spectroscopy (FCS), which relies on analyzing the fluctuations in fluorescence intensity signal to measure the diffusion coefficient of particles, has typically been employed in most of the prior studies. However, flaws in the FCS method, due to its high sensitivity to the environment, have recently been evaluated, calling the prior diffusion results into question. It behooves us to adopt complimentary and direct methods to measure the mobility of enzymes in solution. Herein, we use a novel technique of direct single-molecule imaging to observe the diffusion of single enzymes. This technique is less sensitive to intensity fluctuations and gives the diffusion coefficient directly based on the trajectory of the enzymes. Our measurements recapitulate that enzyme diffusion is enhanced in the presence of its substrate and find that the relative increase in diffusion of a single enzyme is even higher than those previously reported using FCS. We also use this complementary method to test if the total enzyme concentration affects the relative increase in diffusion and if enzyme oligomerization state changes during catalytic turnover. We find that the diffusion increase is independent of the total background concentration of enzyme and the catalysis of substrate does not change the oligomerization state of enzymes.

q-bio.SC↗