2 citations · 3 across the 7 of their papers we have counts for
9 papers
Doubly robust estimators of the restricted mean time in favor estimands in individual- and cluster-randomized trials
Xi Fang, Bingkai Wang, Guangyu Tong +3
Progressive multi-state survival outcomes are common in trials with recurrent or sequential events and require treatment effect estimands that remain interpretable without proporti…
Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes
Xi Fang, Bingkai Wang, Liangyuan Hu +1
Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to interventi…
Model-robust standardization in stepped wedge cluster randomized trials
Xi Fang, Xueqi Wang, Patrick J. Heagerty +2
Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, enabling all clusters to receive the intervention through a staggered ro…
Model-robust standardization in cluster-randomized trials
Fan Li, Jiaqi Tong, Xi Fang +3
In cluster-randomized trials, generalized linear mixed models and generalized estimating equations have conventionally been the default analytic methods for estimating the average…
Asymptotic inference with flexible covariate adjustment under rerandomization and stratified rerandomization
Bingkai Wang, Fan Li
Rerandomization is an effective treatment allocation procedure to control for baseline covariate imbalance. For estimating the average treatment effect, rerandomization has been pr…
How to achieve model-robust inference in stepped wedge trials with model-based methods?
Bingkai Wang, Xueqi Wang, Fan Li
A stepped wedge design is a unidirectional crossover design where clusters are randomized to distinct treatment sequences. While model-based analysis of stepped wedge designs is st…
Handling incomplete outcomes and covariates in cluster-randomized trials: doubly-robust estimation, efficiency considerations, and sensitivity analysis
Bingkai Wang, Fan Li, Rui Wang
In cluster-randomized trials (CRTs), missing data can occur in various ways, including missing values in outcomes and baseline covariates at the individual or cluster level, or com…
Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximations
Bingkai Wang, Fan Li, Mengxin Yu
Traditional statistical inference in cluster randomized trials typically invokes the asymptotic theory that requires the number of clusters to approach infinity. In this article, w…
Test-negative designs with various reasons for testing: statistical bias and solution
Mengxin Yu, Tom Hongyi Liu, Kendrick Qijun Li +5
Test-negative designs are widely used for post-market evaluation of vaccine effectiveness, particularly in cases when randomized trials are not feasible. Differing from classical t…