156 citations · 292 across the 9 of their papers we have counts for
7 papers · 1 filter
A General Form of Covariate Adjustment in Randomized Clinical Trials
Marlena S. Bannick, Jun Shao, Jingyi Liu +3
In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies th…
Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials
Ting Ye, Jun Shao, Yanyao Yi +1
In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model…
Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods
Ting Ye, Yanyao Yi, Jun Shao
Covariate-adaptive randomization schemes such as the minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across progno…
Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization
Ting Ye, Jun Shao, Hyunseung Kang
Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challen…
Nonparametric Estimation of Conditional Expectation with Auxiliary Information and Dimension Reduction
Bingying Xie, Jun Shao
Nonparametric estimation of the conditional expectation of an outcome given a covariate vector is of primary importance in many statistical applications such as…
Sufficient variable screening via directional regression with censored response
Menghao Xu, Zhou Yu, Jun Shao
We in this paper propose a directional regression based approach for ultrahigh dimensional sufficient variable screening with censored responses. The new method is designed in a mo…