activity
20172022
most citedMinorization-Maximization-based Steepest Ascent for Large-scale Survival Analysis with Time-Varying Effects: Application to the National Kidney Transplant Dataset

1 citations · 2 across the 4 of their papers we have counts for

collaborators

7 papers

stat.ME20221 cited

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…

stat.ME2020

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.…

stat.CO20191 cited

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…

stat.AP2019

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…

stat.ML2018

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…

stat.CO2018

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…