arXiv subjects

Chiung-Yu Huang

Publications and source records attributed to Chiung-Yu Huang.

7 recordsLinked to original sources

arXiv2026

Integration of Individual Participant and Aggregate Data Under Dataset Shift: Summary Statistic Comparison and Scalable Computation

Integrated IPD-AD analysis, which combines individual participant data (IPD) with aggregate data (AD), is increasingly recognized as an effective strategy for generating more reliable and generalizable inferences from heterogeneous studies. While most existing work has focused on algorithmic approaches, this paper investigates a complementary yet underexplored question: how different forms of AD influence the efficiency of data integration. Working within a constrained maximum likelihood estimation framework, we compare commonly reported summary statistics and show that subgroup-specific summaries can substantially improve estimation efficiency. In particular, we find that AD derived from outcome-stratified subgroups (e.g., cases and controls) consistently yield greater efficiency gains than those based on covariate-stratified subgroups (e.g., age or exposure categories), especially when the outcome is continuous. Although outcome-stratified summaries are commonly reported for discrete outcomes, they are rarely provided when the outcome is continuous. Our findings therefore support the routine inclusion of outcome-stratified summaries for continuous endpoints in trial reports and public data repositories to facilitate more efficient evidence synthesis. We further extend the constrained maximum likelihood framework to accommodate dataset shift and develop a fast, non-iterative estimation procedure to improve numerical stability and scalability. We illustrate the proposed methodology with two applications: an analysis of income data under covariate shift and an analysis of housing data under prior probability shift.

stat.ME↗

arXiv2026

Transportable inference using target population summary statistics under covariate shift

Transporting findings from a study population to a target population is central to evidence-based decision-making in real-world settings. Most existing methods require individual-level data from both populations to account for covariate shift. However, privacy regulations and data-sharing constraints often preclude access to such data from the target population, leaving only covariate summaries available for analysis. In this paper, we develop transportability methods that enable valid inference using source individual-level data and target covariate summaries. Firstly, we apply entropy balancing to transportability, enabling source individual-level data to be adjusted to match the target covariate moments. We establish asymptotic normality for the entropy balancing estimator and propose a variance estimator to account for uncertainty in covariate summaries. Secondly, we develop a new transportability method that allows flexible modeling of covariate shift, thereby accounting for covariate shift and uncertainty in covariate summaries simultaneously. Asymptotic normality for the proposed estimator is established and its asymptotic variance is consistently estimated. The proposed method offers greater flexibility in accounting for covariate shift and thus permits consistent estimation and valid inference under weaker conditions than those required by entropy balancing. The proposed methods are evaluated by simulations and illustrated with an analysis of Surveillance, Epidemiology, and End Results breast cancer data.

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arXiv2025

Revamping Conformal Selection With Optimal Power: A Neyman--Pearson Perspective

This paper introduces a novel conformal selection procedure, inspired by the Neyman--Pearson paradigm, to maximize the power of selecting qualified units while maintaining false discovery rate (FDR) control. Existing conformal selection methods may yield suboptimal power due to their reliance on conformal p-values, which are derived by substituting unobserved future outcomes with thresholds set by the null hypothesis. This substitution invalidates the exchangeability between imputed nonconformity scores for test data and those derived from calibration data, resulting in reduced power. In contrast, our approach circumvents the need for conformal p-values by constructing a likelihood-ratio-based decision rule that directly utilizes observed covariates from both calibration and test samples. The asymptotic optimality and FDR control of the proposed method are established under a correctly specified model, and modified selection procedures are introduced to improve power under model misspecification. The proposed methods are computationally efficient and can be readily extended to handle covariate shifts, making them well-suited for real-world applications. Simulation results show that these methods consistently achieve comparable or higher power than existing conformal p-value-based selection rules, particularly when the underlying distribution deviates from location-shift models, while effectively maintaining FDR control.

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arXiv2022

Dynamic Risk Prediction Triggered by Intermediate Events Using Survival Tree Ensembles

With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction using survival tree ensembles, where an updated prediction can be performed when new information becomes available. Compared to conventional landmark prediction with fixed landmark times, our methods allow the landmark times to be subject-specific and triggered by an intermediate clinical event. Moreover, the nonparametric approach circumvents the thorny issue of model incompatibility at different landmark times. In our framework, both the longitudinal predictors and the event time outcome are subject to right censoring, and thus existing tree-based approaches cannot be directly applied. To tackle the analytical challenges, we propose a risk-set-based ensemble procedure by averaging martingale estimating equations from individual trees. Extensive simulation studies are conducted to evaluate the performance of our methods. The methods are applied to the Cystic Fibrosis Patient Registry (CFFPR) data to perform dynamic prediction of lung disease in cystic fibrosis patients and to identify important prognosis factors.

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arXiv2022

Regression Modeling for Recurrent Events Using R Package reReg

Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg (Chiou and Huang 2021) offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, possibly with the presence of an informative terminal event. The regression framework is a general scale-change model which encompasses the popular Cox-type model, the accelerated rate model, and the accelerated mean model as special cases. Informative censoring is accommodated through a subject-specific frailty without no need for parametric specification. Different regression models are allowed for the recurrent event process and the terminal event. Also included are visualization and simulation tools.

stat.CO↗

arXiv2020

Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records

Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient's health status influences when and what data are recorded, generating sampling bias in the collected data. In this paper, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases, time-dependent covariates are intermittently measured during clinical visits, and the timing of these visits is informative in the sense that it depends on the disease course. Simple methods, such as the last-observation-carried-forward approach, can lead to biased estimation. On the other hand, complex joint models require additional assumptions on the covariate process and cannot be easily extended to handle multiple longitudinal predictors. By incorporating sampling weights derived from estimating the observation time process, we develop a novel estimation procedure based on inverse-rate-weighting and kernel-smoothing for the semiparametric proportional rate model of recurrent events. The proposed methods do not require model specifications for the covariate processes and can easily handle multiple time-dependent covariates. Our methods are applied to a kidney transplant study for illustration.

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arXiv2018

Quantile Regression Modeling of Recurrent Event Risk

Progression of chronic disease is often manifested by repeated occurrences of disease-related events over time. Delineating the heterogeneity in the risk of such recurrent events can provide valuable scientific insight for guiding customized disease management. In this paper, we present a new modeling framework for recurrent event data, which renders a flexible and robust characterization of individual multiplicative risk of recurrent event through quantile regression that accommodates both observed covariates and unobservable frailty. The proposed modeling requires no distributional specification of the unobservable frailty, while permitting the exploration of dynamic covariate effects. We develop estimation and inference procedures for the proposed model through a novel adaptation of the principle of conditional score. The asymptotic properties of the proposed estimator, including the uniform consistency and weak convergence, are established. Extensive simulation studies demonstrate satisfactory finite-sample performance of the proposed method. We illustrate the practical utility of the new method via an application to a diabetes clinical trial that explores the risk patterns of hypoglycemia in Type 2 diabetes patients.

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