2 citations · 3 across the 7 of their papers we have counts for
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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…