10 citations · 28 across the 5 of their papers we have counts for
6 papers
Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects
Larry Han, Jue Hou, Kelly Cho +2
Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecific…
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach
Sai Li, Tianxi Cai, Rui Duan
The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research has become a barrier to translating precision medicine research…
On the global identifiability of logistic regression models with misclassified outcomes
Rui Duan, Yang Ning, Jiasheng Shi +3
In the last decade, the secondary use of large data from health systems, such as electronic health records, has demonstrated great promise in advancing biomedical discoveries and i…
Testing for publication bias in meta-analysis under Copas selection model
Rui Duan, Jin Piao, Arielle Marks-Anglin +5
In meta-analyses, publication bias is a well-known, important and challenging issue because the validity of the results from a meta-analysis is threatened if the sample of studies…
Missing at Random or Not: A Semiparametric Testing Approach
Rui Duan, C. Jason Liang, Pamela Shaw +2
Practical problems with missing data are common, and statistical methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus…
Heterogeneity-aware and communication-efficient distributed statistical inference
Rui Duan, Yang Ning, Yong Chen
In multicenter research, individual-level data are often protected against sharing across sites. To overcome the barrier of data sharing, many distributed algorithms, which only re…