131 citations · 352 across the 94 of their papers we have counts for
97 papers · 1 filter
Bridge-Anchored Partial Identification of a Target Mean under Outcome-Model Drift
Danhyang Lee, Shinyoung Jeon, Shu Yang
Transporting an outcome relationship from a source population to a target population where the outcome is unobserved requires the conditional outcome law to be stable across popula…
Power and Sample Size Calculations for Hybrid Controlled Trials
Ke Zhu, Shu Yang, Xiaofei Wang
Hybrid controlled trials (HCTs) augment randomized controls with external controls (ECs) to address practical challenges in randomized controlled trials (RCTs) and improve statisti…
Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Ke Zhu, Hairong Huang, Shu Yang +1
Hybrid controlled trials (HCTs) augment randomized controlled trials (RCTs) with external controls (ECs) to improve statistical efficiency when RCTs face limited sample sizes, slow…
History-Aware Conformal Prediction Sets for Censored Time-to-Event Outcomes
Yuyao Wang, Alexander W. Levis, Shu Yang +1
Existing conformal prediction methods for time-to-event outcomes leverage only baseline covariates, producing prediction intervals that are insufficiently informative to facilitate…
Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens
Ke Zhu, Rima Izem, Peng Yang +8
Externally controlled trials (ECTs) are increasingly used when randomized controls are infeasible, unethical, or insufficient, including applications in rare diseases, oncology, pe…
A Robust Framework for Two-Sample Mendelian Randomization under Population Heterogeneity
Dingke Tang, Xuming He, Shu Yang
Mendelian randomization is a powerful tool for causal inference in observational studies. The two-sample summary-data design, which estimates genetic associations with exposures an…
Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls
Qinwei Yang, Jingyi Li, Peng Wu +1
Randomized controlled trials (RCTs) often suffer from limited inferential efficiency in estimating treatment effects due to their small sample sizes. In recent years, incorporating…
Sample-split REGression SREG: A robust estimator for high-dimensional survey data
Yonghyun Kwon, Shu Yang, Jae Kwang Kim
Model-assisted regression estimation is fundamental in survey sampling for incorporating auxiliary information. However, when the auxiliary dimension grows with the sample size, th…
Spatial Causal Tensor Completion for Multiple Exposures and Outcomes: An Application to the Health Effects of PFAS Pollution
Xiaodan Zhou, Brian J Reich, Shu Yang
Per- and polyfluoroalkyl substances (PFAS) are typically encountered as mixtures of distinct chemicals with distinct effects on multiple health outcomes. Estimating joint causal ef…
Selective Information Borrowing for Region-Specific Treatment Effect Inference under Covariate Mismatch in Multi-Regional Clinical Trials
Chenxi Li, Ke Zhu, Shu Yang +1
Multi-regional clinical trials (MRCTs) are central to global drug development, enabling evaluation of treatment effects across diverse populations. A key challenge is valid and eff…
Estimating optimal interpretable individualized treatment regimes from a classification perspective using adaptive LASSO
Yunshu Zhang, Shu Yang, Wendy Ye +2
Real-world data (RWD) gains growing interests to provide a representative sample of the population for selecting the optimal treatment options. However, existing complex black box…
Designing Efficient Hybrid and Single-Arm Trials: External Control Borrowing and Sample Size Calculation
Yujing Gao, Xiang Zhang, Shu Yang
External controls (ECs) from historical trials or real-world data have gained increasing attention as a way to augment hybrid and single-arm trials, especially when balanced random…
Heterogeneity-Aware Federated Causal Inference Leveraging Effect-Measure Transportability
Siqi Cao, Shu Yang
Federated learning of causal estimands offers a powerful strategy to improve estimation efficiency by leveraging data from multiple study sites while preserving privacy. Existing l…
Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis
Shu Yang, Margaret Gamalo, Haoda Fu
Randomized controlled trials (RCTs)have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external vali…
Multivariate Zero-Inflated Causal Model for Regional Mobility Restriction Effects on Consumer Spending
Taekwon Hong, Wenbin Lu, Shu Yang +1
The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, r…
Rate doubly robust estimation for weighted average treatment effects
Yiming Wang, Yi Liu, Shu Yang
The weighted average treatment effect (WATE) defines a versatile class of causal estimands for populations characterized by propensity score weights, including the average treatmen…
A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes
Shih-Ni Prim, Yawen Guan, Shu Yang +4
Characterizing social vulnerability is fundamental to disaster response planning. Numerous vulnerability indicators have been developed, but they are typically not validated for th…
Doubly Robust Fusion of Many Treatments for Policy Learning
Ke Zhu, Jianing Chu, Ilya Lipkovich +2
Individualized treatment rules/recommendations (ITRs) aim to improve patient outcomes by tailoring treatments to the characteristics of each individual. However, when there are man…
Robust Estimation and Inference in Hybrid Controlled Trials for Binary Outcomes: A Case Study on Non-Small Cell Lung Cancer
Jiajun Liu, Ke Zhu, Shu Yang +1
Hybrid controlled trials (HCTs), which augment randomized controlled trials (RCTs) with external controls (ECs), are increasingly receiving attention as a way to address limited po…
Integrative Analysis of High-dimensional RCT and RWD Subject to Censoring and Hidden Confounding
Xin Ye, Shu Yang, Xiaofei Wang +1
In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimens…
Doubly robust omnibus sensitivity analysis of externally controlled trials with intercurrent events
Chenyin Gao, Xiang Zhang, Shu Yang
Externally controlled trials are crucial in clinical development when randomized controlled trials are unethical or impractical. These trials consist of a full treatment arm with t…
Statistical Inference for Heterogeneous Treatment Effect with Right-censored Data from Synthesizing Randomized Clinical Trials and Real-world Data
Guangcai Mao, Shu Yang, Xiaofei Wang
The heterogeneous treatment effect plays a crucial role in precision medicine.There is evidence that real-world data, even subject to biases, can be employed as supplementary evide…
Targeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials
Yi Liu, Alexander W. Levis, Ke Zhu +3
The Antibody Mediated Prevention (AMP) trials opened a new scientific frontier by showing that passively administered monoclonal broadly neutralizing antibodies (bnAbs) could preve…
COADVISE: Covariate Adjustment with Variable Selection in Randomized Controlled Trials
Yi Liu, Ke Zhu, Larry Han +1
Adjusting for covariates in randomized controlled trials can enhance the credibility and efficiency of treatment effect estimation. However, handling numerous covariates and their…