معرفی
Trang Nguyen is an Associate Research Professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health, with a joint affiliation in Biostatistics. She is a member of the Stuart Lab and focuses on advancing causal inference methods for public health research, particularly in mental health, HIV/AIDS, and social justice.
Her educational background includes a PhD and MHS from Johns Hopkins Bloomberg School of Public Health (2021, 2014), an MS from Harvard School of Public Health (2001), and a BA from Ha Noi Foreign Trade University (1995). Her training was guided by prominent advisors including Karen Bandeen-Roche, Liz Stuart, Betsy Ogburn, Constantine Frangakis, Amy Knowlton, and Renee Johnson.
Nguyen's research centers on causal inference, with key interests in mediation analysis, treatment effect heterogeneity, missing data, measurement error, sensitivity analysis, and generalizability. She develops and applies rigorous statistical methods to address data limitations and complex causal questions in observational studies.
Her recent publications (2019–2025) show a strong trend in applying machine learning and advanced statistical models—such as causal forests and principal stratification—to real-world public health problems, especially in HIV care, substance use treatment, and health disparities. The articles reflect interdisciplinary collaboration and methodological innovation.
- Application of Causal Forest Model to Examine Treatment Effect Heterogeneity in Substance Use Disorder Psychosocial Treatments
- Practical challenges in mediation analysis: a guide for applied researchers
- Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects
- Depression and associated factors among HIV-positive smokers receiving care at HIV outpatient clinics in Vietnam
- Estimation of place-based vulnerability scores for HIV viral non-suppression
She has contributed to open science through R packages such as mediationClarity, PIsens, and latentMAR, which implement estimators for mediation, sensitivity analysis, and latent missingness models. These tools support transparent and reproducible causal inference.
Nguyen was originally from Vietnam, where she worked in public health and community development before pursuing advanced training in the U.S. She continues to apply her expertise to global health challenges, particularly in low-resource settings.
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