activity
20162023
most citedA Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications to a Critical Care Trial

22 citations · 25 across the 5 of their papers we have counts for

collaborators

6 papers

stat.ME2023

Bayesian pathway analysis over brain network mediators for survival data

Xinyuan Tian, Fan Li, Li Shen +2

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze br…

stat.ME2023★ 1 cited

Principal Stratification with Time-to-Event Outcomes

Bo Liu, Lisa Wruck, Fan Li

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal strati…

stat.AP2022★ 22 cited

A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications to a Critical Care Trial

Xinyuan Chen, Michael O. Harhay, Guangyu Tong +1

Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to e…

stat.ME2022★ 1 cited

Bayesian semi-parametric inference for clustered recurrent events with zero-inflation and a terminal event/4163305

Xinyuan Tian, Maria Ciarleglio, Jiachen Cai +4

Recurrent event data are common in clinical studies when participants are followed longitudinally, and are often subject to a terminal event. With the increasing popularity of larg…

stat.ME2021

Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model

Xinyuan Chen, Denise Esserman, Fan Li

A population-averaged additive subdistribution hazards model is proposed to assess the marginal effects of covariates on the cumulative incidence function and to analyze correlated…

stat.ME2016★ 1 cited

Disease Mapping with Generative Models

Feifei Wang, Jian Wang, Alan E. Gelfand +1

Disease mapping focuses on learning about areal units presenting high relative risk. Disease mapping models for disease counts specify Poisson regressions in relative risks compare…