Selected work

Representative Papers

Application of Propensity Score Models and Causal Estimators in Observational Studies under Model Misspecification

May 19, 2026

This study systematically evaluates the robustness of response surface modeling (RSM), inverse probability weighting (IPW), and augmented inverse probability weighting (AIPW) under various misspecification scenarios of propensity score and outcome models in observational studies. Leveraging multiple methods—including logistic regression, random forests, support vector machines, and linear discriminant analysis—to estimate propensity scores, the authors assess performance through extensive simulations and real-world applications to the ACTG175 and ADNI datasets. Findings indicate that AIPW demonstrates consistent robustness across most settings, benefiting from its double-robustness property; IPW proves highly sensitive to propensity score misspecification, while RSM performs well only when the outcome model is correctly specified. The results underscore that integrating flexible machine learning techniques within a doubly robust framework substantially enhances the reliability of causal effect estimation.

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Tensegrity crutches with compliance from a pre-stressed self-tensile module improve ground reaction force profiles, speed, effort, comfort, and perceived stability

May 04, 2026

This study addresses the limitations of conventional rigid canes, which lack axial compliance and may impair proprioception while increasing the risk of upper-limb secondary injuries, as well as spring-based canes that offer compliance at the expense of stability. The authors propose a novel cane tip module incorporating a prestressed, self-tensioned double-unit tensegrity structure to achieve synergistic optimization of nonlinear stiffness, ground conformity, and force feedback. Through axial loading tests, human gait experiments—including straight walking and turning maneuvers—and subjective user evaluations, the design demonstrates significant reductions in impact loading rate compared to rigid canes, along with improved comfort, pain relief, perceived exertion, and usability. Crucially, it avoids the stability loss and gait slowing commonly associated with spring-based alternatives.

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Joint analysis for multivariate longitudinal and event time data with a change point anchored at interval-censored event time

Feb 16, 2026

This study addresses the challenge of modeling the bidirectional association between interval-censored onset times and multivariate longitudinal biomarkers in Huntington’s disease clinical research. The authors propose a novel joint model that, for the first time, incorporates an anchored change-point mechanism within an interval-censored event time framework, dynamically coupling biomarker trajectories with the timing of disease onset. This approach not only quantifies the influence of biomarkers on disease risk but also captures structural shifts in biomarker trajectories following event occurrence, thereby enabling bidirectional causal inference between longitudinal processes and event time. Simulation studies demonstrate favorable performance under finite-sample settings, and application to the PREDICT-HD cohort reveals dynamic interactions between cognitive and motor dysfunction throughout disease progression.

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Improving operating characteristics of clinical trials by augmenting control arm using propensity score-weighted borrowing-by-parts power prior

Jan 07, 2026

This study addresses the challenge of bias arising from covariate distributional shifts or outcome heterogeneity when borrowing external control data in clinical trials. To mitigate this, the authors propose the PSW-BPP framework, which uniquely integrates propensity score weighting with partitioned power priors. The approach aligns population distributions via causal covariate adjustment and introduces separate borrowing parameters for the mean and variance components of the outcome model. A novel minimum plausibility index (mPI) is incorporated to automatically calibrate the strength of borrowing, thereby enhancing robustness against prior-data conflict. Simulation studies and real-data analyses demonstrate that, under moderate covariate imbalance and outcome heterogeneity, PSW-BPP substantially improves estimation efficiency and stability compared to strategies that either forgo borrowing or employ fixed borrowing strengths.

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Online multi-layer FDR control

Jun 03, 2025

Controlling both within-group and global false discovery rates (FDR) simultaneously in streaming multi-layer hypothesis testing—e.g., real-time identification of effective nucleic acid–nanocarrier combinations in RNA nanocapsule studies with dual stratification by nucleic acid type and delivery vehicle—remains an open challenge. Method: We propose the first online multi-layer FDR control framework, integrating adaptive p-value weighting with hierarchical dependency modeling. Leveraging martingale theory, we provide a rigorous proof that the method guarantees modified FDR (mFDR) ≤ α at every layer under arbitrary dependency structures among hypotheses. Contribution/Results: Compared to conventional single-layer online methods, our approach significantly improves statistical power and real-time responsiveness in simulations, while accommodating both nested and parallel grouping architectures. The framework establishes a provably valid, deployable paradigm for online multiple testing in cross-dimensional biomedical decision-making.

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Recent publications

Latest Papers

Application of Propensity Score Models and Causal Estimators in Observational Studies under Model Misspecification

May 19, 2026

This study systematically evaluates the robustness of response surface modeling (RSM), inverse probability weighting (IPW), and augmented inverse probability weighting (AIPW) under various misspecification scenarios of propensity score and outcome models in observational studies. Leveraging multiple methods—including logistic regression, random forests, support vector machines, and linear discriminant analysis—to estimate propensity scores, the authors assess performance through extensive simulations and real-world applications to the ACTG175 and ADNI datasets. Findings indicate that AIPW demonstrates consistent robustness across most settings, benefiting from its double-robustness property; IPW proves highly sensitive to propensity score misspecification, while RSM performs well only when the outcome model is correctly specified. The results underscore that integrating flexible machine learning techniques within a doubly robust framework substantially enhances the reliability of causal effect estimation.

0 citationsRead paper

Tensegrity crutches with compliance from a pre-stressed self-tensile module improve ground reaction force profiles, speed, effort, comfort, and perceived stability

May 04, 2026

This study addresses the limitations of conventional rigid canes, which lack axial compliance and may impair proprioception while increasing the risk of upper-limb secondary injuries, as well as spring-based canes that offer compliance at the expense of stability. The authors propose a novel cane tip module incorporating a prestressed, self-tensioned double-unit tensegrity structure to achieve synergistic optimization of nonlinear stiffness, ground conformity, and force feedback. Through axial loading tests, human gait experiments—including straight walking and turning maneuvers—and subjective user evaluations, the design demonstrates significant reductions in impact loading rate compared to rigid canes, along with improved comfort, pain relief, perceived exertion, and usability. Crucially, it avoids the stability loss and gait slowing commonly associated with spring-based alternatives.

0 citationsRead paper

Joint analysis for multivariate longitudinal and event time data with a change point anchored at interval-censored event time

Feb 16, 2026

This study addresses the challenge of modeling the bidirectional association between interval-censored onset times and multivariate longitudinal biomarkers in Huntington’s disease clinical research. The authors propose a novel joint model that, for the first time, incorporates an anchored change-point mechanism within an interval-censored event time framework, dynamically coupling biomarker trajectories with the timing of disease onset. This approach not only quantifies the influence of biomarkers on disease risk but also captures structural shifts in biomarker trajectories following event occurrence, thereby enabling bidirectional causal inference between longitudinal processes and event time. Simulation studies demonstrate favorable performance under finite-sample settings, and application to the PREDICT-HD cohort reveals dynamic interactions between cognitive and motor dysfunction throughout disease progression.

0 citationsRead paper

Improving operating characteristics of clinical trials by augmenting control arm using propensity score-weighted borrowing-by-parts power prior

Jan 07, 2026

This study addresses the challenge of bias arising from covariate distributional shifts or outcome heterogeneity when borrowing external control data in clinical trials. To mitigate this, the authors propose the PSW-BPP framework, which uniquely integrates propensity score weighting with partitioned power priors. The approach aligns population distributions via causal covariate adjustment and introduces separate borrowing parameters for the mean and variance components of the outcome model. A novel minimum plausibility index (mPI) is incorporated to automatically calibrate the strength of borrowing, thereby enhancing robustness against prior-data conflict. Simulation studies and real-data analyses demonstrate that, under moderate covariate imbalance and outcome heterogeneity, PSW-BPP substantially improves estimation efficiency and stability compared to strategies that either forgo borrowing or employ fixed borrowing strengths.

0 citationsRead paper

Online multi-layer FDR control

Jun 03, 2025

Controlling both within-group and global false discovery rates (FDR) simultaneously in streaming multi-layer hypothesis testing—e.g., real-time identification of effective nucleic acid–nanocarrier combinations in RNA nanocapsule studies with dual stratification by nucleic acid type and delivery vehicle—remains an open challenge. Method: We propose the first online multi-layer FDR control framework, integrating adaptive p-value weighting with hierarchical dependency modeling. Leveraging martingale theory, we provide a rigorous proof that the method guarantees modified FDR (mFDR) ≤ α at every layer under arbitrary dependency structures among hypotheses. Contribution/Results: Compared to conventional single-layer online methods, our approach significantly improves statistical power and real-time responsiveness in simulations, while accommodating both nested and parallel grouping architectures. The framework establishes a provably valid, deployable paradigm for online multiple testing in cross-dimensional biomedical decision-making.

0 citationsRead paper