2 citations · 2 across the 18 of their papers we have counts for
21 papers
Nonparametric heterogeneous causal mediation with orthogonal machine learning
Jiaqi Tong, Yi Zhao, Bhramar Mukherjee +1
Causal mediation analysis decomposes the total effect of an intervention on an outcome into a direct pathway and an indirect pathway transmitted through a mediator, but standard me…
Orthogonal double residual learning for optimal individualized treatment rules
Jiaqi Tong, Fan Li
Individualized treatment rules (ITRs) map baseline characteristics to treatment recommendations, with the optimal ITR maximizing expected reward or policy welfare. Indirect methods…
Doubly robust estimation of while-alive estimands in individually-randomized and cluster-randomized trials
Xi Fang, Da Zhao, Fan Li
Randomized trials in chronic disease settings often measure treatment benefit through recurrent non-fatal events that are truncated by death, where conventional summaries either di…
Doubly cross-fit debiased machine learning of heterogeneous treatment effects under principal stratification
Jiaqi Tong, Fan Li
Principal stratification provides a foundational framework for causal inference with intermediate outcomes by defining causal effects within subpopulations, yet existing work has l…
Sensitivity analysis for causal mediation: bridge score, sharp sensitivity bounds, and calibration
Yuki Ohnishi, Fan Li
Causal mediation analysis decomposes the total treatment effect into a portion operating through a hypothesized mediator and a residual direct portion. Identification of natural di…
Sample size and power calculations for causal inference with time-to-event outcomes
Chengxin Yang, Bo Liu, Fan Li
This paper develops power and sample size formulas for causal inference with time-to-event outcomes. The target estimand is the marginal hazard ratio: the coefficient of a marginal…
Causal inference with ordinal outcomes: copula-based identification, estimation and sensitivity analysis
Peiyu He, Fan Li
In causal inference with ordinal outcomes, several interpretable estimands are functions of the probability that the potential outcome under one treatment is larger than that under…
Statistical inference with win statistics in cluster-randomized trials with hierarchical composite outcomes
Xi Fang, Guangyu Tong, Yuan Huang +3
Win statistics have become increasingly popular for analyzing hierarchical composite endpoints in clinical trials. The win ratio, win odds, net benefit, and desirability of outcome…
Which Small-Sample Correction Should Be Used When Analyzing Stepped-Wedge Designs with Time-Varying Treatment Effects?
Yongdong Ouyang, Monica Taljaard, James P. Hughes +1
Stepped-wedge cluster randomized trials (SW-CRTs) evaluate interventions rolled out across clusters over time. Standard analyses typically use immediate-treatment (IT) models, whic…
Leveraging machine learning to estimate individualized treatment effects in cluster-randomized trials
Changjun Li, Xi Fang, Michael O. Harhay +4
Cluster-randomized trials (CRTs) are widely used to evaluate interventions delivered at the clinic, practice, or community level. Although standard analyses typically target averag…
Nonparametric efficient inference for network quantile causal effects under partial interference
Chao Cheng, Fan Li
Interference arises when the treatment assigned to one individual affects the outcomes of other individuals. Commonly, individuals are naturally grouped into clusters, and interfer…
Emulating Stepped-Wedge Cluster Randomized Trials to Evaluate Health Policies and Interventions
Haidong Lu, Gregg S. Gonsalves, Fan Li +2
Both cluster randomized trials and quasi-experimental designs are used to evaluate the impact of health and social policies and interventions. Stepped-wedge cluster randomized tria…
Causal mediation in cluster-randomized trials with multiple mediators: spillover-aware decomposition, identification, and semiparametric efficient inference
Jiaqi Tong, Chao Cheng, Fan Li
Causal mediation analysis in cluster-randomized trials (CRTs) is complicated by the presence of multiple mediators, intracluster correlation, and within-cluster interference. Exist…
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…