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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.

Alisa Stephens Shields, PhD serves as an Associate Professor in the Biostatistics division of the Department of Biostatistics, Epidemiology and Informatics, and is recognized for her collaboration and impactful leadership in the realms of health, statistical education, and inclusion in the field.

Alisa Stephens Shields, PhD, Associate Professor of Biostatistics, was selected by the Harvard T.H. Chan School of Public Health to receive the 2024 Myrto Lefkopoulou Distinguished Lectureship.

Before joining Harvard University as an Assistant Professor of Biostatistics, Rui Duan, PhD, completed her PhD in Biostatistics at the University of Pennsylvania.

Edward Kennedy, PhD is a Penn alum through and through—prior to completing his PhD in Biostatistics in 2016, he earned his BA in Mathematics (2007) and MA in Statistics (2014) from the University.

Dr. Yang’s methodological research includes causal inference, functional data analysis and dynamic risk prediction. He is also interested in collaborative research in chronic kidney disease, cardiovascular disease and pharmacoepidemiology.

Dr. Zee’s research focuses on developing and applying methods for observational and survival data analysis and identifying novel biomarkers of kidney disease progression. She collaborates on clinical research projects in […]

Dr. Stephens Shields is an Associate Professor of Biostatistics. Her research is focused on extensions and innovative applications of causal inference approaches to enhance the design and analysis of clinical […]

As University Professor, Dr. Tchetgen Tchetgen holds joint primary appointments in the Department of Statistics and Data Science at The Wharton School and in the Department of Biostatistics, Epidemiology and […]

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. Mumford is renowned for her research on modifiable dietary and lifestyle factors affecting reproductive health, which has informed guidelines for women, men, and couples. Her work uses couples-based approaches […]

Dr. Schaubel’s methodologic research interests mostly involve survival analysis and the analysis of recurrent event data. His more recent methodology has been at the intersection of causal inference and survival […]

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; […]

Yun Li, PhD is Associate Professor of Biostatistics in the Perelman School of Medicine at University of Pennsylvania’s Department of Biostatistics, Epidemiology and Informatics (DBEI), and a faculty member at […]

Dr. Linn’s methodological research addresses barriers to incorporating complex, high-dimensional data into models for personalized medicine and biomarker development using neuroimaging data. As a co-investigator, she provides biostatistical leadership on […]

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. Miano is a critical care pharmacist and epidemiologist whose work aims to reduce harm from adverse drug events in acutely ill patients. His research focuses on understanding the determinants […]

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 […]

Dr. Hsu’s statistical research focuses on methods for observational studies and causal inference. He has specific interests in survival analysis, longitudinal data analysis, inverse probability treatment weighting, and the potential […]

Dr. Huang’s research focuses on methodology development to understand the dynamics of disease activities and inform health management using multivariate longitudinal health data. She is currently the PI of a […]

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. Jones is a critical care physician and genetic epidemiologist who investigates the genetic determinants of acute respiratory failure. Her primary research interest is the elucidation of the molecular and […]

Dr. Leonard is an Associate Professor of Epidemiology in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine. He serves as a faculty and Executive Committee […]

Working with Penn collaborators, we are currently developing methods for analysis of high-throughout genomic data. Our application areas include genome-wide association studies of neuroblastoma, eQTL analysis of human heart failure […]

Dr. Getz is a pediatric cancer epidemiologist whose research aims to improve outcomes for children and young adults with cancer. Her research has focused on understanding the occurrence and subsequent […]

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. Cohen’s primary research interest is in the area of hypertension. Her research program focuses on better understanding physiology-driven pharmacologic effects of antihypertensive medications in complex and high-risk disease states […]

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. Caniglia is a perinatal and HIV epidemiologist who works to improve health outcomes among pregnant people and their children, and among people with HIV. Her work utilizes methods for […]

Nandita Mitra, PhD, Professor of Biostatistics, has been awarded the 2024 L. Adrienne Cupples Award for her outstanding contributions to research, teaching, and leadership in biostatistics and public health.

“Pregnant women CAN drink coffee – it could even slash risk of disease for mum and baby,” proclaimed a headline in the English paper The Sun about a recent study […]

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 […]

Many human diseases start out with single cells, which establish the problem by spreading out. Spatial transcriptomics, a groundbreaking method, allows scientists to take advantage of that activity, measuring all […]

Covid-19 vaccines work, but a statistical illusion makes some people think they don’t. This perspective piece prominently cites work by Jeffrey Morris, PhD.   Read the perspective in the Washington Post

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 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 […]

Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions Chanmin Kim, Corwin M Zigler, Michael J Daniels, Christine Choirat, and Jason A Roy Pollutant […]

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 […]

Robust causal inference with continuous instruments using the local instrumental variable curve Edward H. Kennedy, Scott Lorch, and Dylan S. Small Instrumental variables are commonly used to estimate effects of […]

Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables Wang L, Tchetgen Tchetgen E. Instrumental variables (IVs) are widely used for estimating causal effects in the […]

Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders Elizabeth Handorf, Daniel Heitjan, Justin Bekelman, Nandita Mitra Link The analysis of observational data to determine the cost-effectiveness […]

Estimating scaled treatment effects with multiple outcomes Edward H Kennedy, Shreya Kangovi, Nandita Mitra Link In classical study designs, the aim is often to learn about the effects of a […]

Bayesian nonparametric generative models for causal inference with missing at random covariates Jason Roy, Kirsten J. Lum, Bret Zeldow , Jordan D. Dworkin, Vincent Lo Re III, and Michael J. […]

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 […]

Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available. Ertefaie A, Small D, Flory J, Hennessy S. Instrumental variable (IV) methods […]

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 […]