arXiv subjects

Lu Tian

Publications and source records attributed to Lu Tian.

At least 19 recordsLinked to original sources

arXiv2026

Interpreting Hierarchical Composite Endpoints with Survival and Longitudinal Outcomes: Application to Amyotrophic Lateral Sclerosis Trials

Hierarchical composite endpoints combining survival and longitudinal functional outcomes are increasingly used in clinical trials, especially when death precludes subsequent functional assessment. The Finkelstein--Schoenfeld strategy analyzes such endpoints through prioritized pairwise comparisons, with survival compared before function. In amyotrophic lateral sclerosis (ALS), this strategy is implemented in the Combined Assessment of Function and Survival (CAFS), which combines survival with the ALS Functional Rating Scale Revised. Although such endpoints provide a clinically meaningful summary of overall treatment benefit, investigators may also want to understand whether the treatment effect is driven by survival, functional outcome, or both. Motivated by the estimand framework, we use ALS-informed simulations from a joint longitudinal--survival model to study settings in which treatment effects on survival and function align or conflict. The simulations show that decomposing the composite win probability into survival and survivor-based functional contributions clarifies their relative roles and separates the composite treatment-benefit question from function-focused questions, including the while-alive comparison and conceptual alternatives based on hypothetical and always-survivor estimands. The functional win probability underlying the while-alive comparison can be subject to survivor-selection bias when treatment affects survival, and inverse-probability weighting can attenuate selection induced by measured predictors under appropriate assumptions. Brief supporting analyses based on principal stratification and multiply robust estimation illustrate the always-survivor estimand. This framework provides practical guidance for reporting and interpreting hierarchical composite endpoints, with CAFS in ALS serving as a concrete motivating example.

stat.ME↗

arXiv2026

RECaST-Surv: A Calibrated Borrowing Method for Survival Endpoints in Unequal Randomized Trials

Randomized trials with limited concurrent control information---including but not limited to unequal-randomization settings---can offer ethical and practical advantages, especially in pediatric and rare diseases, but they often lose power because fewer control patients are available for direct comparison. Borrowing information from external controls may improve efficiency, but can also inflate the Type I error rate when the external and trial populations are not sufficiently comparable. We propose RECaST-Surv, a Bayesian transfer-learning framework for time-to-event outcomes that extends the RECaST method to survival settings. The method learns a structural survival model from external control data and calibrates it to the concurrent control arm of the current trial through a Cauchy random effect. To improve frequentist operating characteristics, we further develop a bootstrap-based procedure to calibrate the testing rule for Type I error control. RECaST-Surv can accommodate multiple external datasets and requires only summary-level information from external sources. Simulation studies show that the method maintains near-nominal Type I error across challenging settings while improving power by roughly 10%--12% over standard analyses. In an Amyotrophic Lateral Sclerosis trial emulation, RECaST-Surv increased power from 82.8% to 95.7% relative to the no-borrowing RCT analysis, while maintaining acceptable error control.

stat.ME↗

arXiv2026

Learned Look-Ahead Splitting Rule for CART

Classification and regression trees are typically constructed using a greedy splitting rule that maximizes the immediate reduction in prediction error at each node. Although this strategy is computationally efficient, it can miss splits that yield small short-term gains but create substantial downstream improvements after further partitioning. We propose a look-ahead tree-building method that evaluates each candidate split by the prediction error reduction achieved after growing a conventional CART subtree below that split. Because the full look-ahead procedure can be computationally expensive, we also describe a smart look-ahead algorithm that learns downstream split values using node-level features. The proposed framework preserves the interpretability of recursive partitioning while improving split selection in hierarchical or interaction-driven settings. We conduct a simulation study comparing conventional, full look-ahead, and smart look-ahead methods under several settings and apply the proposed methods to analyze two real data examples demonstrating the merit of the new methods.

stat.ML↗

arXiv2026

TAMS: Task-Aware Multi-View Adaptive Streaming for Wireless Telerobotic Manipulation

Wireless telerobotic manipulation relies on timely multi-view video feedback, but the available uplink bandwidth is often limited and dynamic. This paper presents Task-Aware Multi-View Adaptive Streaming (TAMS), a system that allocates video bitrate according to the current manipulation phase. TAMS infers task phase from lightweight robot-side signals and prioritizes the camera view most relevant to the operator while preserving baseline visibility for secondary views. Experiments on a six-degree-of-freedom (6-DoF) teleoperation testbed under three constrained network conditions show that TAMS improves primary view Structural Similarity Index (SSIM), reduces task completion time, and increases trial success rate compared with equal and static allocation baselines. Under the most constrained bandwidth condition, TAMS reduces mean completion time from 68.9 s to 43.9 s relative to equal allocation and increases trial success rate from 48% to 71%. Code is available at: https://github.com/Dzxx623/TAMS.

cs.RO↗

arXiv2026

Average Cause-Specific Hazard: A Censoring-Invariant Measure of Event Burden Under Competing Risks

Competing events are common in clinical and epidemiologic studies, including semi-competing risks in which a terminal event such as death may follow a nonfatal event but also competes with it beforehand. Standard summaries include the cumulative incidence function (CIF) and the incidence rate (IR), defined as the number of observed events divided by observed event-free person-time. With competing events, the naive IR generally depends on the censoring-time distribution unless intensities are constant. We propose the Average Cause-Specific Hazard (ACSH), a survival-weighted rate per event-free person-time that preserves the interpretation of an incidence rate and is defined purely from the event-time distribution, without involving the censoring-time distribution. We develop nonparametric estimation and inference for ACSH and, for two-sample comparisons, introduce ACSH differences and ratios that provide interpretable contrasts without requiring a strong model assumption between two groups. Simulation studies examine the finite-sample performance, and an analysis of the CANVAS trial illustrates the proposed methods.

stat.ME↗

arXiv2026

Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization

Many clinical risk scores are deployed as additive rules with nonnegative integer points assigned to relevant binary predictive features. These integer weights not only make the score easier to use in practice but also promote sparsity in the resulting prediction model. Such risk scores are often derived by first fitting a regression model and then rounding the estimated coefficients to the nearest integer after appropriate scaling. This approach is computationally fast but does not guarantee optimality of the resulting score. Alternatively, one may search over all possible integer weights to directly optimize a value function by posing the problem as an integer programming task. However, the associated computational burden can be substantial, especially when the value function is nonconcave or even discontinuous. In this paper, we develop new machine learning algorithms that employ a flexible greedy optimization strategy to learn such additive scoring directly under explicit and sensible optimality objectives. We apply the proposed method to a large electronic health record (EHR) cohort in Epic Cosmos to construct an integer-weighted comorbidity score for measuring the risk of post-discharge mortality. We also conduct a simulation study to examine the finite-sample operating characteristics.

stat.ME↗

arXiv2026

Cost-optimal Sequential Testing via Doubly Robust Q-learning

Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness. Under a sequential missing-at-random mechanism, we develop a doubly robust Q-learning framework for estimating optimal policies. The method introduces path-specific inverse probability weights that account for heterogeneous test trajectories and satisfy a normalization property conditional on the observed history. By combining these weights with auxiliary contrast models, we construct orthogonal pseudo-outcomes that enable unbiased policy learning when either the acquisition model or the contrast model is correctly specified. We establish oracle inequalities for the stage-wise contrast estimators, along with convergence rates, regret bounds, and misclassification rates for the learned policy. Simulations demonstrate improved cost-adjusted performance over weighted and complete-case baselines, and an application to a prostate cancer cohort study illustrates how the method reduces testing cost without compromising predictive accuracy.

stat.ML↗

arXiv2026

Bayesian-Enhanced Galerkin-Based Reduced Order Modelling for Unsteady Compressible Flows

This work proposes a statistically enhanced framework to address the instability and limited predictive capability of conventional Galerkin-Proper Orthogonal Decomposition (Galerkin-POD) models. The method reformulates the correction of the Galerkin-projected ODE system as a statistical inverse problem, in which the coefficients are inferred through Bayesian inference. By accounting for model uncertainty arising from POD mode truncation and data uncertainty introduced by data noise and numerical postprocessing, the framework systematically updates the ODE system coefficients using an analytical, sampling-free solution based on Gaussian likelihood and inverse-Gamma priors. The approach is first validated using a self-sustained oscillating flow over a dimpled surface at a moderate Reynolds number (Re=3000), demonstrating stable and accurate reproduction of the temporal dynamics and phase trajectories of coherent structures when compared with direct numerical simulation (DNS). It is then applied to a centrifugal compressor featuring strong tip-leakage vortex breakdown and impeller-diffuser interactions at Re=100000, where the model successfully captures dominant unsteady structures and frequency characteristics despite limited mode retention. Overall, the results show that Bayesian inference substantially enhances the robustness, stability, and predictive fidelity of Galerkin-POD models for compressible flow systems. The proposed methodology combines the physical interpretability of Galerkin projection with the statistical rigour of Bayesian inference, offering a general, computationally efficient, and uncertainty-aware reduced-order modelling framework for complex fluid dynamic applications.

physics.flu-dyn↗

arXiv2026

Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events

As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can be quantified by the timing and occurrence of multiple non-fatal clinical events. Most existing methods for the analysis of multiple event-time data require stringent modeling assumptions that can be difficult to verify empirically, leading to treatment efficacy estimates that forego interpretability when the underlying assumptions are not met. Moreover, many methods do not appropriately account for informative terminal events, such as premature treatment discontinuation or death, which prevent the occurrence of subsequent events. To address these limitations, we derive and validate estimation and inference procedures for the area under the mean cumulative function (AUMCF), an extension of the restricted mean survival time to the multiple event-time setting. The AUMCF is clinically interpretable, properly accounts for terminal competing risks, and can be estimated nonparametrically. To enable covariate adjustment, we also develop an augmentation estimator that provides efficiency at least equaling, and often exceeding, the unadjusted estimator. The utility and interpretability of the AUMCF are illustrated with extensive simulation studies and through an analysis of multiple heart-failure-related endpoints using data from the Beta-Blocker Evaluation of Survival Trial (BEST) clinical trial. Our open-source R package MCC makes conducting AUMCF analyses straightforward and accessible.

stat.ME↗

arXiv2026

Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs

Hybrid neural ordinary differential equations (neural ODEs) integrate mechanistic models with neural ODEs, offering strong inductive bias and flexibility, and are particularly advantageous in data-scarce healthcare settings. However, excessive latent states and interactions from mechanistic models can lead to training inefficiency and over-fitting, limiting practical effectiveness of hybrid neural ODEs. In response, we propose a new hybrid pipeline for automatic state selection and structure optimization in mechanistic neural ODEs, combining domain-informed graph modifications with data-driven regularization to sparsify the model for improving predictive performance and stability while retaining mechanistic plausibility. Experiments on synthetic and real-world data show improved predictive performance and robustness with desired sparsity, establishing an effective solution for hybrid model reduction in healthcare applications.

cs.LG↗

arXiv2026

Efficient and Debiased Learning of Average Hazard Under Non-Proportional Hazards

The hazard ratio from the Cox proportional hazards model is a ubiquitous summary of treatment effect. However, when hazards are non-proportional, the hazard ratio can lose a stable causal interpretation and become study-dependent because it effectively averages time-varying effects with weights determined by follow-up and censoring. We consider the average hazard (AH) as an alternative causal estimand: a population-level person-time event rate that remains well-defined and interpretable without assuming proportional hazards. Although AH can be estimated nonparametrically and regression-style adjustments have been proposed, existing approaches do not provide a general framework for flexible, high-dimensional nuisance estimation with valid sqrt{n} inference. We address this gap by developing a semiparametric, doubly robust framework for covariate-adjusted AH. We establish pathwise differentiability of AH in the nonparametric model, derive its efficient influence function, and construct cross-fitted, debiased estimators that leverage machine learning for nuisance estimation while retaining asymptotically normal, sqrt{n}-consistent inference under mild product-rate conditions. Simulations demonstrate that the proposed estimator achieves small bias and near-nominal confidence-interval coverage across proportional and non-proportional hazards settings, including crossing-hazards regimes where Cox-based summaries can be unstable. We illustrate practical utility in comparative effectiveness research by comparing immunotherapy regimens for advanced melanoma using SEER-Medicare linked data.

stat.ME↗

arXiv2026

Giant Magnetocaloric Effect in a High-Spin Shastry-Sutherland Dipolar Magnet

The Shastry-Sutherland lattice is a prototypical frustrated quantum magnet. It is notable for its exactly solvable dimer-singlet ground state and hosts a wealth of magnetic phenomena under external fields. Here, this work investigates the high-spin (S = 7/2) Eu-based magnet Eu2MgSi2O7 (EMSO) using low-temperature magnetothermal measurements and Monte Carlo simulations, revealing a giant magnetocaloric effect (MCE) in this Shastry-Sutherland compound. The entropy change peak value is found to be 55.0 J kg-1 K-1 under a field change of B = 0-4 T, approximately 1.5 times larger than the commercial Gd3Ga5O12 (GGG). Adiabatic demagnetization refrigeration achieves a lowest temperature of 151 mK, deeply into the sub-Kelvin regime. Furthermore, a distinctive cooling effect persists below about 1 T, a characteristic absent for conventional magnetic coolants. A dipolar Shastry-Sutherland model is introduced as a minimal model to describe this system; in particular, the experimentally revealed 1/3 magnetization pseudo-plateau can be ascribed to the presence of dipolar couplings between Eu2+ ions, further stabilized by the thermal fluctuations, explaining the persistent cooling effect. This work establishes EMSO as a novel platform for exploring the dipolar Shastry-Sutherland system and for sub-Kelvin adiabatic demagnetization refrigeration.

cond-mat.mtrl-sci↗

arXiv2026

GRIP2: A Robust and Powerful Deep Knockoff Method for Feature Selection

Identifying truly predictive covariates while strictly controlling false discoveries remains a fundamental challenge in nonlinear, highly correlated, and low signal-to-noise regimes, where deep learning based feature selection methods are most attractive. We propose Group Regularization Importance Persistence in 2 Dimensions (GRIP2), a deep knockoff feature importance statistic that integrates first-layer feature activity over a two-dimensional regularization surface controlling both sparsity strength and sparsification geometry. To approximate this surface integral in a single training run, we introduce efficient block-stochastic sampling, which aggregates feature activity magnitudes across diverse regularization regimes along the optimization trajectory. The resulting statistics are antisymmetric by construction, ensuring finite-sample FDR control. In extensive experiments on synthetic and semi-real data, GRIP2 demonstrates improved robustness to feature correlation and noise level: in high correlation and low signal-to-noise ratio regimes where standard deep learning based feature selectors may struggle, our method retains high power and stability. Finally, on real-world HIV drug resistance data, GRIP2 recovers known resistance-associated mutations with power better than established linear baselines, confirming its reliability in practice.

cs.LG↗

arXiv2026

Optical Self-Trapping and Nonlinear Light-Matter Interactions in Biological Soft Matter

Low-scattering, deep-penetration light transport in biological media remains a pivotal challenge for biophotonic technologies, including biomedical imaging, optical diagnostics, and photodynamic therapy. This review builds upon and extends our earlier studies of nonlinear optical self-trapping and optically induced waveguiding in biological suspensions, such as human erythrocytes and cyanobacteria, where light-matter coupling is governed by optical-force-mediated particle redistribution. Recent progress has revealed increasingly rich and complex regimes, including the propagation and nonlinear self-action of structured (vortex) beams in biological environments, as well as nonlinear responses dominated by thermally driven mechanisms in absorptive biomolecular solutions (e.g., heme and chlorophyll). We place particular emphasis on distinctive nonlinear phenomena observed in these systems, including spatial self-phase modulation, optical-force-induced sculpturing of effective energy landscapes, and quasi-waveguide formation in soft, heterogeneous biological media. We conclude by highlighting emerging opportunities to harness these nonlinear behaviors for deep-tissue imaging, label-free biosensing, and the realization of biocompatible photonic structures and devices assembled directly from living or hybrid biological matter.

physics.optics↗

arXiv2025

A Honest Cross-Validation Estimator for Prediction Performance

Cross-validation is a standard tool for obtaining a honest assessment of the performance of a prediction model. The commonly used version repeatedly splits data, trains the prediction model on the training set, evaluates the model performance on the test set, and averages the model performance across different data splits. A well-known criticism is that such cross-validation procedure does not directly estimate the performance of the particular model recommended for future use. In this paper, we propose a new method to estimate the performance of a model trained on a specific (random) training set. A naive estimator can be obtained by applying the model to a disjoint testing set. Surprisingly, cross-validation estimators computed from other random splits can be used to improve this naive estimator within a random-effects model framework. We develop two estimators -- a hierarchical Bayesian estimator and an empirical Bayes estimator -- that perform similarly to or better than both the conventional cross-validation estimator and the naive single-split estimator. Simulations and a real-data example demonstrate the superior performance of the proposed method.

stat.ML↗

arXiv2025

Bootstrapping the Cross-Validation Estimate

Cross-validation is a widely used technique for evaluating the performance of prediction models, ranging from simple binary classification to complex precision medicine strategies. It helps correct for optimism bias in error estimates, which can be significant for models built using complex statistical learning algorithms. However, since the cross-validation estimate is a random value dependent on observed data, it is essential to accurately quantify the uncertainty associated with the estimate. This is especially important when comparing the performance of two models using cross-validation, as one must determine whether differences in estimated error are due to chance. Although various methods have been developed to make inferences on cross-validation estimates, they often have many limitations, such as requiring stringent model assumptions. This paper proposes a fast bootstrap method that quickly estimates the standard error of the cross-validation estimate and produces valid confidence intervals for a population parameter measuring average model performance. Our method overcomes the computational challenges inherent in bootstrapping a cross-validation estimate by estimating the variance component within a random-effects model. It is also as flexible as the cross-validation procedure itself. To showcase the effectiveness of our approach, we conducted comprehensive simulations and real-data analysis across two applications.

stat.ME↗

arXiv2025

Comment on "Average Hazard as Harmonic Mean" by Chiba

In a recent article published in Pharmaceutical Statistics, Chiba proposed a reinterpretation of the average hazard as a harmonic mean of the hazard function and questioned the validity of the Kaplan-Meier plug-in estimator when the truncation time does not coincide with an observed event time. In this commentary, we examine the arguments presented and highlight several points that warrant clarification. Through simulation studies, we further show that the plug-in estimator provides reliable estimates across a range of truncation times, even in small samples. These support the continued utilization of the Kaplan-Meier plug-in estimator for the average hazard and help clarify its proper interpretation and implementation.

stat.ME↗

arXiv2025

Latent Noise Injection for Private and Statistically Aligned Synthetic Data Generation

Synthetic Data Generation has become essential for scalable, privacy-preserving statistical analysis. While standard approaches based on generative models, such as Normalizing Flows, have been widely used, they often suffer from slow convergence in high-dimensional settings, frequently converging more slowly than the canonical $1/\sqrt{n}$ rate when approximating the true data distribution. To overcome these limitations, we propose a Latent Noise Injection method using Masked Autoregressive Flows (MAF). Instead of directly sampling from the trained model, our method perturbs each data point in the latent space and maps it back to the data domain. This construction preserves a one to one correspondence between observed and synthetic data, enabling synthetic outputs that closely reflect the underlying distribution, particularly in challenging high-dimensional regimes where traditional sampling struggles. Our procedure satisfies local $(ε, δ)$-differential privacy and introduces a single perturbation parameter to control the privacy-utility trade-off. Although estimators based on individual synthetic datasets may converge slowly, we show both theoretically and empirically that aggregating across $K$ studies in a meta analysis framework restores classical efficiency and yields consistent, reliable inference. We demonstrate that with a well-calibrated perturbation parameter, Latent Noise Injection achieves strong statistical alignment with the original data and robustness against membership inference attacks. These results position our method as a compelling alternative to conventional flow-based sampling for synthetic data sharing in decentralized and privacy-sensitive domains, such as biomedical research.

stat.ML↗