42 citations · 64 across the 7 of their papers we have counts for
7 papers
Combining Cox Regressions Across a Heterogeneous Distributed Research Network Facing Small and Zero Counts
Martijn J. Schuemie, Yong Chen, David Madigan +1
Studies of the effects of medical interventions increasingly take place in distributed research settings using data from multiple clinical data sources including electronic health…
Generalized Co-sparse Factor Regression
Aditya Mishra, Dipak K. Dey, Yong Chen +1
Multivariate regression techniques are commonly applied to explore the associations between large numbers of outcomes and predictors. In real-world applications, the outcomes are o…
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…
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…
Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models
Ray Bai, Gemma E. Moran, Joseph Antonelli +2
We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse…
Testing small study effects in multivariate meta-analysis
Chuan Hong, Georgia Salanti, Sally Morton +4
Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analy…