40 citations · 53 across the 6 of their papers we have counts for
12 papers
On variance of the treatment effect in the treated using inverse probability weighting
Sarah A. Reifeis, Michael G. Hudgens
In the analysis of observational studies, inverse probability weighting (IPW) is commonly used to consistently estimate the average treatment effect (ATE) or the average treatment…
diproperm: An R Package for the DiProPerm Test
Andrew G. Allmon, J. S. Marron, Michael G. Hudgens
High-dimensional low sample size (HDLSS) data sets emerge frequently in many biomedical applications. A common task for analyzing HDLSS data is to assign data to the correct class…
Power and Sample Size for Marginal Structural Models
Bonnie E. Shook-Sa, Michael G. Hudgens
Marginal structural models fit via inverse probability of treatment weighting are commonly used to control for confounding when estimating causal effects from observational data. W…
Balanced Policy Evaluation and Learning for Right Censored Data
Owen E. Leete, Nathan Kallus, Michael G. Hudgens +2
Individualized treatment rules can lead to better health outcomes when patients have heterogeneous responses to treatment. Very few individualized treatment rule estimation methods…
Estimands and Inference in Cluster-Randomized Vaccine Trials
Kayla W. Kilpatrick, Michael G. Hudgens, M. Elizabeth Halloran
Cluster-randomized trials are often conducted to assess vaccine effects. Defining estimands of interest before conducting a trial is integral to the alignment between a study's obj…
Inverse Probability Weighted Estimators of Vaccine Effects Accommodating Partial Interference and Censoring
Sujatro Chakladar, Michael G. Hudgens, M. Elizabeth Halloran +3
Estimating population-level effects of a vaccine is challenging because there may be interference, i.e., the outcome of one individual may depend on the vaccination status of anoth…