42 citations · 64 across the 9 of their papers we have counts for
9 papers
Sensitivity Analysis of Causal Treatment Effect Estimation for Clustered Observational Data with Unmeasured Confounding
Yang Ou, Lu Tang, Chung-Chou H. Chang
Identifying causal treatment (or exposure) effects in observational studies requires the data to satisfy the unconfoundedness assumption which is not testable using the observed da…
Covariate-guided Bayesian mixture model for multivariate time series
Haoyi Fu, Lu Tang, Ori Rosen +3
With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging play an important role in the advancement of sc…
RISE: Robust Individualized Decision Learning with Sensitive Variables
Xiaoqing Tan, Zhengling Qi, Christopher W. Seymour +1
This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the interv…
Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity
Zhongying Xu, Andriy I. Bandos, Tianzhou Ma +3
Allocating patients to treatment arms during a trial based on the observed responses accumulated prior to the decision point, and sequential adaptation of this allocation,, could m…
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou +1
Accurately estimating personalized treatment effects within a study site (e.g., a hospital) has been challenging due to limited sample size. Furthermore, privacy considerations and…
Outcome-Guided Disease Subtyping for High-Dimensional Omics Data
Peng Liu, Yusi Fang, Zhao Ren +2
High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classic…