Di Shu, PhD



Di Shu, PhD

Assistant Professor of Biostatistics

Di Shu is an Assistant Professor of Biostatistics in the Department of Biostatistics, Epidemiology and Informatics in the Perelman School of Medicine at the University of Pennsylvania. Her research focuses on developing and evaluating suitable statistical methods to help assess safety and effectiveness of medical products with potentially complex data from clinical trials or real-world observational databases.

To address concerns about misspecification of the treatment decision process using a single propensity score model, Dr. Shu and her colleagues have developed a robust method that allows for using a set of propensity score models simultaneously. The resulting estimators of causal effect measures achieve statistical consistency when the set of propensity score models contains a correct one. They also have developed a one-step method to allow data partners to share only summary-level risk set tables to estimate overall and site-specific hazard ratios in distributed data network studies. This method has been implemented as part of the routine querying tools in Sentinel, the U.S. Food and Drug Administration's national medical product safety surveillance system. To correct for outcome misclassification, a common data quality issue, they derived a closed-form, bias-corrected estimator of causal relative risk as well as an efficient method using validation data.

Dr. Shu is also passionate about software development. She and her colleagues have written three packages that are publicly available on the Comprehensive R Archive Network and an online calculator for ROC study sample size planning with precision and assurance.


Other Positions:

Assistant Professor of Biostatistics in Pediatrics
Associate Director for Biostatistics, Clinical Futures
The Children’s Hospital of Philadelphia


Content Area Specialties

Pediatrics, pharmacoepidemiology

Methodology Specialties

Causal inference, clinical trials, real-world evidence

About Us

To understand health and disease today, we need new thinking and novel science —the kind  we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. 

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