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20082025
most citedTransporting Experimental Results with Entropy Balancing

42 citations

Showing stat.MEShow all

10 papers · 1 filter

stat.ME2025

Residual-based Alternative Partial Least Squares for Generalized Functional Linear Models

Yue Wang, Xiao Wang, Joseph G. Ibrahim +1

Many biomedical studies collect high-dimensional medical imaging data to identify biomarkers for the detection, diagnosis, and treatment of human diseases. Consequently, it is cruc…

stat.ME2022★ 1 cited

Fast, effective, and coherent time series modeling using the sparsity-ranked lasso

Ryan Peterson, Joseph Cavanaugh

The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorith…

stat.ME2021★ 11 cited

Ranked Sparsity: A Cogent Regularization Framework for Selecting and Estimating Feature Interactions and Polynomials

Ryan A. Peterson, Joseph E. Cavanaugh

We explore and illustrate the concept of ranked sparsity, a phenomenon that often occurs naturally in modeling applications when an expected disparity exists in the quality of info…

stat.ME2020★ 4 cited

Measurement Error in Meta-Analysis (MEMA) -- a Bayesian framework for continuous outcome data

Harlan Campbell, Valentijn M. T. de Jong, Lauren Maxwell +3

Ideally, a meta-analysis will summarize data from several unbiased studies. Here we consider the less than ideal situation in which contributing studies may be compromised by measu…

stat.ME2020★ 23 cited

A Calibration Approach to Transportability and Data-Fusion with Observational Data

Kevin P. Josey, Fan Yang, Debashis Ghosh +1

Two important considerations in clinical research studies are proper evaluations of internal and external validity. While randomized clinical trials can overcome several threats to…

stat.ME2020★ 42 cited

Transporting Experimental Results with Entropy Balancing

Kevin P. Josey, Seth A. Berkowitz, Debashis Ghosh +1

We show how entropy balancing can be used for transporting experimental treatment effects from a trial population onto a target population. This method is doubly-robust in the sens…

stat.ME2019★ 1 cited

Analysis of regression discontinuity designs using censored data

Youngjoo Cho, Chen Hu, Debashis Ghosh

In medical settings, treatment assignment may be determined by a clinically important covariate that predicts patients' risk of event. There is a class of methods from the social s…

stat.ME2019★ 9 cited

A Framework for Covariate Balance using Bregman Distances

Kevin P. Josey, Elizabeth Juarez-Colunga, Fan Yang +1

A common goal in observational research is to estimate marginal causal effects in the presence of confounding variables. One solution to this problem is to use the covariate distri…

stat.ME2018

Predictive Directions for Individualized Treatment Selection in Clinical Trials

Debashis Ghosh, Youngjoo Cho

In many clinical trials, individuals in different subgroups have experience differential treatment effects. This leads to individualized differences in treatment benefit. In this a…

stat.ME2013

Reducing decision errors in the paired comparison of the diagnostic accuracy of screening tests with Gaussian outcomes

Brandy M. Ringham, Todd A. Alonzo, John T. Brinton +4

Scientists often use a paired comparison of the areas under the receiver operating characteristic curves to decide which continuous cancer screening test has the best diagnostic ac…