most citedTargeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials

1 citations · 1 across the 5 of their papers we have counts for

collaborators

9 papers

stat.ME20261 cited

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…

stat.ME2026

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…

stat.ME2026

A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data

Jiajun Liu, Ke Zhu, Xiaofei Wang

Identifying patients who are likely to benefit from a treatment is central to precision medicine and can guide follow-up trials, enrichment designs, and individualized decisions. A…

stat.ME2026

Fast Rerandomization for Balancing Covariates in Randomized Experiments: A Metropolis-Hastings Framework

Jiuyao Lu, Tianruo Zhang, Ke Zhu

Balancing covariates is critical for credible and efficient randomized experiments. Rerandomization addresses this by repeatedly generating treatment assignments until covariate ba…

stat.ME2026

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…

stat.ME2025

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…