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In Annals of Internal Medicine, Nicholas Seewald, PhD, introduces a framework for target trial emulation, enabling researchers to evaluate health policies with the rigor of clinical trials in nonexperimental studies.

Dr. Xie’s current research goals are twofold. First, she continues to develop new statistical methods for survival analysis, missing data, measurement error problems, high dimensional data, biomarker evaluations, and longitudinal […]

Dr. Putt is a translational scientist, whose goal is to both bring state-of-the-art statistical methodologies to the design and analysis of biomedical research studies, and to adapt/develop methodologies to meet […]

Dr. Schisterman is a national leader in reproductive epidemiology and epidemiological methods. His complementary training in epidemiology and statistics has enabled him to improve study designs; to fuel new methods; […]

Nicholas J. Seewald, PhD is an Assistant Professor in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine at the University of Pennsylvania. Previously, Dr. Seewald […]

Dr. Long’s research purposefully includes novel statistical and machine learning research and impactful biomedical research, each of which reinforces the other. Its thrust is to develop statistical and machine learning […]

Dr. Mitra’s primary research interests include the design and analysis of observational studies, causal inference, and statistical approaches for cost-effectiveness analysis. She has developed doubly robust approaches to estimation of […]

In addition to his professorial role, Dr. Morris serves as Director of the Division of Biostatistics. His research interests focus on developing quantitative methods to extract knowledge from biomedical big […]

Dr. Lo Re is an infectious diseases physician and clinical epidemiologist whose areas of interest include epidemiology of hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, HIV-viral hepatitis […]

Dr. Jin’s research focuses on developing statistical methods and computational tools to address cutting-edge problems in public health and medicine by integrating large-scale, multi-source datasets. Her research areas include health […]

Dr. Ky’s research interests center on translational cardiovascular epidemiology and focus particularly on novel signaling pathways and mechanisms in human heart failure, cardiac remodeling, and cancer therapy cardiotoxicity. Her current […]

Dr. Levy works at the interface of epidemiology, ecology, and statistics to understand and control vector-borne and other infectious diseases. For over two decades he has focused his research on […]

Dr. Gimotty’s research focuses on statistical methods and their applications in cancer translational research, prevention and control, and risk assessment. She has served as the biostatistics core director for the […]

Dr. Guo joined the Biostatistics faculty at the University of Pennsylvania in 1998, and was promoted to Professor of Biostatistics in 2010. He was elected as ASA fellow in 2010 […]

Dr. Michael Harhay specializes in the statistical design, analysis, and interpretation of large randomized trials and cohort studies. At Penn, he oversees a broad research portfolio, including serving as the […]

Dr. Himes’ work focuses on using biomedical informatics approaches to study asthma and other complex traits. Dr. Himes began asthma genetics and pharmacogenetics research by participating in genome-wide association studies […]

John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director for Medical Informatics at the […]

Dr. Chen’s research focuses primarily on the development of statistical methods for designing and analyzing two-phase epidemiologic studies, on methodological and collaborative work in genetic-association studies of complex diseases, and […]

Dr. Yong Chen is Professor of Biostatistics at Department of Biostatistics, Epidemiology, and Informatics (DBEI), and a Senior Scholar at Center for Clinical Epidemiology & Biostatistics (CCEB) at the University […]

Dr. Farrar has been involved in clinical research for more than 30 years, with a major focus on the study of the efficacy of pain therapeutics and on novel methodology […]

Ongoing Methodological Research: Dr. Feng’s current research focuses on exploring and improving the potential of deep learning methodologies, for structured input variables in biomedical research. Her recent development of the […]

Dr. Aplenc is a Professor of Pediatrics in the Department of Pediatrics with a secondary appointment in the Department of Biostatistics, Epidemiology and Informatics. His research focus is in pediatric […]

Brielin C. Brown is an Assistant Professor of Informatics and Genetics in the Perelman School of Medicine who is broadly interested in the development and application of statistical and computational […]

Dr. Castillo’s work focuses on the development and application of innovative epidemiological methods to understand, control, and eliminate infectious diseases. He conducts research on zoonotic diseases in Latin America where […]

The Community Engagement and Research Core (CEAR) at the University of Pennsylvania has awarded pilot grant funding for 2023-2024. Two projects were selected to receive funding through the CEAR Pilot […]

The Built Environment Assessment Training (BEAT) Institute grew out of the recognition that environmental and policy changes are some of the most promising strategies for controlling obesity and improving diet […]

The poster presenting the results from the healthy eating study conducted by Karen Glanz, PhD, MPH; Amanda Fultz, PhD, RDN; Yolande Goncalves, MPH; Pui Kwong, MPH; and Christina Roberto, PhD., […]

The National Academies held a public release webinar of a new report on Tuesday, August 9 from 1:00pm to 2:00pm EDT. The report, Review of Fate, Exposure, and Effects of Sunscreens in Aquatic […]

In addition to making events such as catastrophic flooding more frequent, climate change will negatively affect human health in many other ways. Prior research has demonstrated, for instance, that high […]

A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations […]

Doubly robust nonparametric instrumental variable estimators for survival outcomes Youjin Lee, Edward H Kennedy, Nandita Mitra Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal […]

Given rising rates of obesity in the U.S. and the many associated health problems, researchers have tested various strategies for weight loss. Both financial incentives and environmental changes — such […]

While many studies have looked at how to limit patients to a short course of opioids, few have examined the other side of the issue: Are there some patients for […]

Job Vacancies and Immigration: Evidence from the Mariel Supply Shock L. Jason Anastasopoulos, George J. Borjas, Gavin G. Cook, and Michael Lachanski We use the Conference Board’s Help-Wanted Index (HWI) […]

Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, Dylan S. Small Unmeasured confounding is a key threat […]

Bayesian Nonparametric Cost-Effectiveness Analyses: Causal Estimation and Adaptive Subgroup Discovery Arman Oganisian, Nandita Mitra, Jason Roy Cost-effectiveness analyses (CEAs) are at the center of health economic decision making. While these […]

A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya Maria Cuellar and Edward H. Kennedy Current estimation methods for the probability of causation […]

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes […]

Spatial extent inference (SEI) is widely used across neuroimaging modalities to adjust for multiple comparisons when studying brain-phenotype associations that inform our understanding of disease. Recent studies have shown that […]

A Bayesian Nonparametric Model for Zero-Inflated Outcomes: Prediction, Clustering, and Causal Estimation Arman Oganisian, Nandita Mitra, and Jason Roy Researchers are often interested in predicting outcomes, conducting clustering analysis to […]

Patterns of Effects and Sensitivity Analysis for Differences-in-Differences Luke J. Keele, Dylan S. Small, Jesse Y. Hsu, and Colin B. Fogarty Applied analysts often use the differences-in-differences (DID) method to […]

In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most […]

In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of […]

A new, powerful approach to the study of effect modification in observational studies Kwonsang Lee, Dylan S. Small, Paul R. Rosenbaum Effect modification occurs when the magnitude or stability of […]

The Trend-in-Trend Research Design for Causal Inference. Ji X, Small DS, Leonard CE, Hennessy S. Cohort studies can be biased by unmeasured confounding. We propose a hybrid ecologic-epidemiologic design called […]

Constructed Second Control Groups and Attenuation of Unmeasured Biases Samuel D. Pimentel, Dylan S. Small & Paul R. Rosenbaum The informal folklore of observational studies claims that if an irrelevant […]

An adaptive Mantel-Haenszel test for sensitivity analysis in observational studies. Rosenbaum PR, Small DS In a sensitivity analysis in an observational study with a binary outcome, is it better to […]

An evaluation of bias in propensity score-adjusted non-linear regression models Fei Wan, Nandita Mitra Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment […]