Michael G. Hudgens

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University of North Carolina at Chapel Hill

On variance of the treatment effect in the treated using inverse probability weighting

In the analysis of observational studies, inverse probability weighting ...
11/24/2020 ∙ by Sarah A. Reifeis, et al. ∙ 0 ∙ share

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diproperm: An R Package for the DiProPerm Test

High-dimensional low sample size (HDLSS) data sets emerge frequently in ...
08/30/2020 ∙ by Andrew G. Allmon, et al. ∙ 0 ∙ share

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Power and Sample Size for Marginal Structural Models

Marginal structural models fit via inverse probability of treatment weig...
03/12/2020 ∙ by Bonnie E. Shook-Sa, et al. ∙ 0 ∙ share

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Balanced Policy Evaluation and Learning for Right Censored Data

Individualized treatment rules can lead to better health outcomes when p...
11/13/2019 ∙ by Owen E. Leete, et al. ∙ 0 ∙ share

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Estimands and Inference in Cluster-Randomized Vaccine Trials

Cluster-randomized trials are often conducted to assess vaccine effects....
10/08/2019 ∙ by Kayla W. Kilpatrick, et al. ∙ 0 ∙ share

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Inverse Probability Weighted Estimators of Vaccine Effects Accommodating Partial Interference and Censoring

Estimating population-level effects of a vaccine is challenging because ...
10/08/2019 ∙ by Sujatro Chakladar, et al. ∙ 0 ∙ share

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Exact Power of the Rank-Sum Test for a Continuous Variable

Accurate power calculations are essential in small studies containing ex...
01/14/2019 ∙ by Katie R. Mollan, et al. ∙ 0 ∙ share

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Post-randomization Biomarker Effect Modification in an HIV Vaccine Clinical Trial

While the HVTN 505 trial showed no overall efficacy of the tested vaccin...
11/09/2018 ∙ by Peter B. Gilbert, et al. ∙ 0 ∙ share

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Doubly Robust Estimation in Observational Studies with Partial Interference

Interference occurs when the treatment (or exposure) of one individual a...
06/19/2018 ∙ by Lan Liu, et al. ∙ 0 ∙ share

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Randomization inference with general interference and censoring

Interference occurs between individuals when the treatment (or exposure)...
03/06/2018 ∙ by Wen Wei Loh, et al. ∙ 0 ∙ share

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Average treatment effects in the presence of unknown interference

We investigate large-sample properties of treatment effect estimators un...
11/17/2017 ∙ by Fredrik Sävje, et al. ∙ 0 ∙ share

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Causal Inference from Observational Studies with Clustered Interference

Inferring causal effects from an observational study is challenging beca...
11/13/2017 ∙ by Brian G. Barkley, et al. ∙ 0 ∙ share

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Michael G. Hudgens

Featured Co-authors

Nathan Kallus

Michael R. Kosorok

J. S. Marron

Mohammad Ali

Peter B. Gilbert

Bryan E. Shepherd

Peter M. Aronow

Wen Wei Loh

M. Elizabeth Halloran

Fredrik Sävje

Lan Liu

On variance of the treatment effect in the treated using inverse probability weighting

diproperm: An R Package for the DiProPerm Test

Power and Sample Size for Marginal Structural Models

Balanced Policy Evaluation and Learning for Right Censored Data

Estimands and Inference in Cluster-Randomized Vaccine Trials

Inverse Probability Weighted Estimators of Vaccine Effects Accommodating Partial Interference and Censoring

Exact Power of the Rank-Sum Test for a Continuous Variable

Post-randomization Biomarker Effect Modification in an HIV Vaccine Clinical Trial

Doubly Robust Estimation in Observational Studies with Partial Interference

Randomization inference with general interference and censoring

Average treatment effects in the presence of unknown interference

Causal Inference from Observational Studies with Clustered Interference