8 papers
Shape-Preserving Covariate Adjustment via Empirical Likelihood in Randomized Experiment
Zhilan Lou, Jun Shao, Yuhan Qian +4
Covariate adjustment improves estimation efficiency in randomized experiments, but standard calibration and augmentation methods, when applied to distribution or survival functions…
Bridging Balancing Weights and Augmentation in Covariate-adjusted Analyses with Time-to-Event Endpoints: Theory and Practical Recommendations
Baoshan Zhang, Yi Chen, Yu Du +1
Covariate adjustment improves the efficiency of treatment-effect analyses in randomized clinical trials, provided the adjustment targets the correct quantity. For time-to-event end…
Covariate Adjustment for Wilcoxon Two Sample Statistic and Test
Zhilan Lou, Jun Shao, Ting Ye +3
We apply covariate adjustment to the Wincoxon two sample statistic and Wincoxon-Mann-Whitney test in comparing two treatments. The covariate adjustment through calibration not only…
Competing Risk Analysis in Cardiovascular Outcome Trials: A Simulation Comparison of Cox and Fine-Gray Models
Tuo Wang, Yu Du
Cardiovascular outcome trials commonly face competing risks when non-CV death prevents observation of major adverse cardiovascular events (MACE). While Cox proportional hazards mod…
Capturing Cumulative Disease Burden in Chronic Kidney Disease Outcome Trials: Area Under the Curve and Restricted Mean Time in Favor of Treatment Beyond Conventional Time-to-First Analysis
Jiren Sun, Tuo Wang, Yu Du
Chronic kidney disease (CKD) affects millions worldwide and progresses irreversibly through stages culminating in end-stage renal disease (ESRD) and death. Outcome trials in CKD tr…
Interpretational challenges of the Win Ratio in analyzing Hierarchical Composite Endpoints in Chronic Kidney Disease
Henrik F. Thomsen, Samvel B. Gasparyan, Julie F. Furberg +7
Win statistics based methods have gained traction as a method for analyzing Hierarchical Composite Endpoints (HCEs) in randomized clinical trials, particularly in cardiovascular an…