Liangyuan Hu

Associate Professor

Rutgers University

United States

Research Interests

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Liangyuan Hu is an Associate Professor at Rutgers The State University of New Jersey, USA. His research focuses on causal inference, treatment effect heterogeneity, and machine learning applications in health studies, as evidenced by his recent publications on survival data and treatment effects in various healthcare contexts, including COVID-19 and cancer outcomes. His work employs innovative methodologies to enhance the understanding of complex healthcare datasets.

Articles (11)

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2024

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2023

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2022

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2022

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2022

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2022

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2022

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2021

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2021

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2021

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2021

Collaborators (4)

Himanshu Joshi

Assistant Professor

Icahn School of Medicine at Mount Sinai

UNITED STATES

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Fan Li

Assistant Professor

Yale School of Public Health

UNITED STATES

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Lihua Li

Mount Sinai Hospital

UNITED STATES

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Yan Li

Associate Professor

Icahn School of Medicine at Mount Sinai

UNITED STATES

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Liangyuan Hu

Research Interests

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About

Articles (11)

A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection

Estimating the causal effects of multiple intermittent treatments with application to COVID-19

Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series

Correlates of cancer prevalence across census tracts in the United States: A Bayesian machine learning approach

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations

A flexible approach for causal inference with multiple treatments and clustered survival outcomes

Immunotherapy Outcomes in Individuals With Non–Small Cell Lung Cancer and Poor Performance Status

Variable selection with missing data in both covariates and outcomes: Imputation and machine learning

Estimating heterogeneous survival treatment effect in observational data using machine learning

Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes

Quantile Regression Forests to Identify Determinants of Neighborhood Stroke Prevalence in 500 Cities in the USA: Implications for Neighborhoods with High Prevalence

Collaborators (4)

Himanshu Joshi

Fan Li

Lihua Li

Yan Li

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