40 citations · 151 across the 21 of their papers we have counts for
23 papers
Fusing Trial Data for Treatment Comparisons: Single versus Multi-Span Bridging
Bonnie E. Shook-Sa, Paul N. Zivich, Samuel P. Rosin +4
While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from tr…
Quantifying the HIV reservoir with dilution assays and deep viral sequencing
Sarah C. Lotspeich, Brian D. Richardson, Pedro L. Baldoni +2
People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these res…
Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference
Chanhwa Lee, Donglin Zeng, Michael G. Hudgens
Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume…
Bridged treatment comparisons: an illustrative application in HIV treatment
Paul N Zivich, Stephen R Cole, Jessie K Edwards +3
Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons.…
Estimating SARS-CoV-2 Seroprevalence
Samuel P. Rosin, Bonnie E. Shook-Sa, Stephen R. Cole +1
Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who…
G-Formula for Observational Studies under Stratified Interference, with Application to Bed Net Use on Malaria
Kayla W. Kilpatrick, Chanhwa Lee, Michael G. Hudgens
Assessing population-level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one perso…
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…
Design and analysis considerations for a sequentially randomized HIV prevention trial
David Benkeser, Keith Horvath, Cathy Reback +2
TechStep is a randomized trial of a mobile health interventions targeted towards transgender adolescents. The interventions include a short message system, a mobile-optimized web a…
Exact Power of the Rank-Sum Test for a Continuous Variable
Katie R. Mollan, Ilana M. Trumble, Sarah A. Reifeis +4
Accurate power calculations are essential in small studies containing expensive experimental units or high-stakes exposures. Herein, exact power of the Wilcoxon Mann-Whitney rank-s…
Post-randomization Biomarker Effect Modification in an HIV Vaccine Clinical Trial
Peter B. Gilbert, Bryan S. Blette, Bryan E. Shepherd +1
While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, previous studies, biological theories, and the finding that immune…
Doubly Robust Estimation in Observational Studies with Partial Interference
Lan Liu, Michael G. Hudgens, Bradley Saul +3
Interference occurs when the treatment (or exposure) of one individual affects the outcomes of others. In some settings it may be reasonable to assume individuals can be partitione…
Randomization inference with general interference and censoring
Wen Wei Loh, Michael G. Hudgens, John D. Clemens +2
Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in t…
Causal Inference from Observational Studies with Clustered Interference
Brian G. Barkley, Michael G. Hudgens, John D. Clemens +2
Inferring causal effects from an observational study is challenging because participants are not randomized to treatment. Observational studies in infectious disease research prese…
Average treatment effects in the presence of unknown interference
Fredrik Sävje, Peter M. Aronow, Michael G. Hudgens
We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the averag…
The Calculus of M-estimation in R with geex
Bradley C. Saul, Michael G. Hudgens
M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find roots and co…
Upstream Causes of Downstream Effects
Bradley C. Saul, Michael G. Hudgens, Michael A. Mallin
The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the U.S. most pressing environmental challenges. But limited independent…
Nonparametric Bounds and Sensitivity Analysis of Treatment Effects
Amy Richardson, Michael G. Hudgens, Peter B. Gilbert +1
This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under…
Nonparametric inference for competing risks current status data with continuous, discrete or grouped observation times
Marloes H. Maathuis, Michael G. Hudgens
New methods and theory have recently been developed to nonparametrically estimate cumulative incidence functions for competing risks survival data subject to current status censori…