Faculty
Elizabeth Ogburn (Betsy) is an Assistant Professor in Biostatistics at the Johns Hopkins Bloomberg School of Public Health. Her research interests include the development of new methods for statistical and causal inference in the presence of interference (when one subject’s treatment may affect other subjects’ outcomes) and ways to deal with complex, non-Euclidean dependence that may be present when observations are sampled from members of a social network.
Michael Rosenblum is an Assistant Professor in Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research areas/interests include adaptive clinical trial designs, robustness to model misspecification, and HIV/AIDS prevention and treatment. Recent Publications:
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Dan Scharfstein is professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
His research is focused on how to draw inferences about treatment effects in the presence of selection bias, specifically how to report results in randomized trials with informative missing or censored data and in observational studies with non-random treatment assignment.
His research is focused on how to draw inferences about treatment effects in the presence of selection bias, specifically how to report results in randomized trials with informative missing or censored data and in observational studies with non-random treatment assignment.
Elizabeth Stuart is an Associate Professor in the Department of Mental Health and the Department of Biostatistics. Her primary research interests are in statistical methodology for mental health research, particularly related to causal inference and missing data. Her current methodological projects focus on best practices for the use of propensity score methods, methods for generalizing treatment effects from randomized trials to target populations, and propensity score methods that can account for measurement error in covariates. Her broad application areas of interest are mental health and education, with a focus on autism, evaluations of mental health policies (such as parity legislation), and suicide. She currently serves as Chair of the Patient Centered Outcomes Research Institute's Clinical Trials Advisory Panel.
Postdocs
Iván Díaz is a Postdoctoral Fellow in the department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
His research focuses on the study of semiparametric efficient estimation, machine learning methods, causal inference, and missing data.
His research focuses on the study of semiparametric efficient estimation, machine learning methods, causal inference, and missing data.
S. Guy Mahiane
Hwanhee Hong is a Postdoctoral Fellow working with Dr.
Elizabeth Stuart in the department of Mental Health at the Johns Hopkins
Bloomberg School of Public Health. Her research interests include propensity score,
measurement error, causal inference, Bayesian hierarchical modeling, network
meta-analysis, comparative effectiveness research, personalized medicine, and
missing data.
Trang Q. Nguyen, a recent JHSPH graduate with a PhD from the HBS department and an MHS from the biostatistics department, is currently a postdoc in the MH department's DDET Program. Working with Elizabeth Stuart, she is trying to learn everything causal inference and discovering the joy of doing simulations. Her current project (with Yenny Webb-Vargas and Liz Stuart) is about causal mediation analysis with multiple mediators.
Kevin Psoter is a Postdoctoral Fellow in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research interests include methods to estimate treatment effects in the absence of randomization, specifically in patients with cystic fibrosis.
Students
Qing Cai is a PhD student in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
Parichoy Pal Choudhury is a fifth year biostatistics PhD student at the Johns Hopkins Bloomberg School of Public Health. He works with Dan Scharfstein. His research is focussed on learning about scientific and causal questions from multiple data sources in scenarios where a single data source is not sufficient to answer the question of interest. He has worked on scientific problems arising in applications from genetic epidemiology, environmental epidemiology, clinical trials and electronic medical records.
David Lenis is a third-year biostatistics PhD student at the Johns Hopkins Bloomberg School of Public Health. He works with Elizabeth Stuart, studying doubly robust estimators under the presence of measurement error and propensity score estimation in case-control studies.
Yi Lu is a PhD Candidate of Biostatistics in Johns Hopkins Bloomberg School of Public Health. Her current research is focused on semi-parametric estimation for treatment effects under various sampling designs.
Tianchen Qian is a third-year PhD student of Biostatistics at Johns Hopkins Bloomberg School of Public Health. He works with Constantine Frangakis on deductive derivation on semiparametric estimator, and on doubly sampling design. He also works Michael Rosenblum on clinical trial design that leverages prognostic information to improve efficiency.
See more at http://tcqian.wordpress.com/.
See more at http://tcqian.wordpress.com/.
Yenny Webb-Vargas is a fifth-year biostatistics PhD student at the Johns Hopkins Bloomberg School of Public Health. She works with Martin Lindquist and Elizabeth Stuart, studying functional causal mediation, designs of experiments for mediation, and measurement error and propensity score estimation.
Zhenke Wu is a Postdoctoral Fellow in the Department of Biostatistics in Johns Hopkins Bloomberg School of Public Health. In the realm of causal inference, he is generally interested in developing new methods for handling missing data and deviation from protocols, which are commonly encountered in medicine, public health and sociology. Recently, he is involved in research projects including:
a) evaluation of treatment effect under matched-pair cluster randomized design with application to guided-care nurse intervention
b) efficient semi-parametric estimation from a computational perspective
His long-term interest is to develop statistical methods to integrate both observational and experimental data for monitoring and improving individual health trajectories.
a) evaluation of treatment effect under matched-pair cluster randomized design with application to guided-care nurse intervention
b) efficient semi-parametric estimation from a computational perspective
His long-term interest is to develop statistical methods to integrate both observational and experimental data for monitoring and improving individual health trajectories.
