arXiv subjects

Mei-Cheng Wang

Publications and source records attributed to Mei-Cheng Wang.

4 recordsLinked to original sources

arXiv2026

Rank-Based Estimation of U-Shaped Biomarker Risk Curves and Critical Points for Time-to-Event Outcomes

U-shaped relationships between prognostic biomarker levels and adverse event risk are commonly observed across diseases, where both low and high biomarker values are associated with elevated risk, with a well-defined minimum -- the critical point -- marking the biomarker value of the lowest risk. The U-shaped risk curve, especially the location of the critical point, informs the identification of high- and low-risk subgroups. However, existing methods are limited: U-shaped risk models rarely accommodate survival outcomes, and existing survival analysis methods do not enable estimation of or formal inference for the critical point. To fill this gap, we propose a semiparametric transformation model that explicitly parameterizes the critical point, a rank-based maximum C-index estimator for the parametric component, and a smoothed Kaplan-Meier estimation approach for the nonparametric component. The resulting framework estimates both subgroup-specific U-shaped risk curves and their critical points within a single survival model. We establish consistency and asymptotic normality of the proposed estimators and demonstrate their finite-sample performance through numerical studies. We apply the proposed methods to UK Biobank data to characterize subgroup-specific U-shaped associations between body mass index and all-cause mortality and to identify the corresponding critical points.

stat.ME↗

arXiv2023

Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates

We propose Bayesian nonparametric Weibull delegate racing (WDR) for survival analysis with competing events and achieve both model interpretability and flexibility. Utilizing a natural mechanism of surviving competing events, we assume a race among a potentially infinite number of sub-events. In doing this, WDR accommodates nonlinear covariate effects with no need of data transformation. Moreover, WDR is able to handle left truncation, time-varying covariates, different types of censoring, and missing event times or types. We develop an efficient MCMC algorithm based on Gibbs sampling for Bayesian inference and provide an \texttt{R} package. Synthetic data analysis and comparison with benchmark approaches demonstrate WDR's outstanding performance and parsimonious nonlinear modeling capacity. In addition, we analyze two real data sets and showcase advantages of WDR. Specifically, we study time to death of three types of lymphoma and show the potential of WDR in modeling nonlinear covariate effects and discovering new diseases. We also use WDR to investigate the age at onset of mild cognitive impairment and interpret the accelerating or decelerating effects of biomarkers on the progression of Alzheimer's disease.

stat.ME↗

arXiv2020

ROC-Guided Survival Trees and Ensembles

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent Receiver Operating Characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.

stat.ME↗

arXiv2010

Backward estimation of stochastic processes with failure events as time origins

Stochastic processes often exhibit sudden systematic changes in pattern a short time before certain failure events. Examples include increase in medical costs before death and decrease in CD4 counts before AIDS diagnosis. To study such terminal behavior of stochastic processes, a natural and direct way is to align the processes using failure events as time origins. This paper studies backward stochastic processes counting time backward from failure events, and proposes one-sample nonparametric estimation of the mean of backward processes when follow-up is subject to left truncation and right censoring. We will discuss benefits of including prevalent cohort data to enlarge the identifiable region and large sample properties of the proposed estimator with related extensions. A SEER--Medicare linked data set is used to illustrate the proposed methodologies.

stat.AP↗