2 citations · 7 across the 13 of their papers we have counts for
13 papers
Robust Privacy-Preserving Models for Cluster-Level Confounding: Recognizing Disparities in Access to Transplantation
Nicholas Hartman, Kevin He
In applications where the study data are collected within cluster units (e.g., patients within transplant centers), it is often of interest to estimate and perform inference on the…
Incorporating External Risk Information with the Cox Model under Population Heterogeneity: Applications to Trans-Ancestry Polygenic Hazard Scores
Di Wang, Wen Ye, Ji Zhu +5
Polygenic hazard score (PHS) models designed for European ancestry (EUR) individuals provide ample information regarding survival risk discrimination. Incorporating such informatio…
Bregman Divergence-Based Data Integration with Application to Polygenic Risk Score (PRS) Heterogeneity Adjustment
Qinmengge Li, Matthew T. Patrick, Haihan Zhang +9
Polygenic risk scores (PRS) have recently received much attention for genetics risk prediction. While successful for the Caucasian population, the PRS based on the minority populat…
Understanding the dynamic impact of COVID-19 through competing risk modeling with bivariate varying coefficients
Wenbo Wu, John D. Kalbfleisch, Jeremy M. G. Taylor +2
The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. Motivated…
Composite Scores for Transplant Center Evaluation: A New Individualized Empirical Null Method
Nicholas Hartman, Joseph M. Messana, Jian Kang +3
Risk-adjusted quality measures are used to evaluate healthcare providers while controlling for factors beyond their control. Existing healthcare provider profiling approaches typic…
Survival Analysis via Ordinary Differential Equations
Weijing Tang, Kevin He, Gongjun Xu +1
This paper introduces an Ordinary Differential Equation (ODE) notion for survival analysis. The ODE notion not only provides a unified modeling framework, but more importantly, als…
Matching methods for obtaining survival functions to estimate the effect of a time-dependent treatment
Yun Li, Douglas E. Schaubel, Kevin He
In observational studies of survival time featuring a binary time-dependent treatment, the hazard ratio (an instantaneous measure) is often used to represent the treatment effect.…
Minorization-Maximization-based Steepest Ascent for Large-scale Survival Analysis with Time-Varying Effects: Application to the National Kidney Transplant Dataset
Kevin He, Ji Zhu, Jian Kang +1
The time-varying effects model is a flexible and powerful tool for modeling the dynamic changes of covariate effects. However, in survival analysis, its computational burden increa…
Accounting for total variation and robustness in profiling health care providers
Lu Xia, Kevin He, Yanming Li +1
Monitoring outcomes of health care providers, such as patient deaths, hospitalizations and hospital readmissions, helps in assessing the quality of health care. We consider a large…
Covariance-Insured Screening
Kevin He, Jian Kang, Hyokyoung Grace Hong +5
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarke…
SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting
Emily Morris, Kevin He, Yanming Li +2
High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables…
Latent Gaussian Mixture Models for Nationwide Kidney Transplant Center Evaluation
Lanfeng Pan, Yehua Li, Kevin He +2
Five year post-transplant survival rate is an important indicator on quality of care delivered by kidney transplant centers in the United States. To provide a fair assessment of ea…
Classification with Ultrahigh-Dimensional Features
Yanming Li, Hyokyoung Hong, Jian Kang +3
Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensi…