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E-mail
shud [at] chop.edu
Location - People View
4th Floor, Room 4472

2716 South Street
Philadelphia, PA 19146
United States

Research Topics
Di Shu, PhD
Di Shu, PhD
Assistant Professor of Biostatistics

Dr. Shu's research focuses on developing and applying suitable statistical methods for assessing comparative safety and effectiveness of medical products.

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Bio

Dr. Shu is the associate director for Biostatistics at the Clinical Futures, a CHOP Research Institute Center of Emphasis, and a Biostatistics faculty member at Children’s Hospital of Philadelphia. She is also an assistant professor of Biostatistics in the Department of Biostatistics, Epidemiology and Informatics, and an investigator in the Center for Pharmacoepidemiology Research and Training, in the Perelman School of Medicine at the University of Pennsylvania.

Dr. Shu is interested in developing and applying suitable statistical methods to assess comparative safety and effectiveness of drugs and other medical products with real-world data — including electronic health records and administrative claims data — which may have complex structures that complicate statistical analysis.

Her research areas include causal inference, measurement error, pharmacoepidemiology, and privacy-protecting methods. 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 simultaneously using a set of propensity score models. 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.

Education and Training

PhD, University of Waterloo (Statistics), 2018

MSc, University of Western Ontario (Statistics with specialization in Biostatistics), 2014

BSc, South China University of Technology (Mathematics and Applied Mathematics), 2013

Titles and Academic Titles

Assistant Professor of Biostatistics

Associate Director for Biostatistics, Clinical Futures

Professional Memberships

Statistical Society of Canada, 2015-

American Statistical Association, 2019-

International Society for Pharmacoepidemiology, 2019-

International Chinese Statistical Association, 2021-

Publication Highlights

Pubmed:
Shu D, Han P, Wang R, Toh S. Estimating the marginal hazard ratio by simultaneously using a set of propensity score models: A multiply robust approach. Stat Med. 2021 Feb; 40(5):1224-1242. Epub 2021 Jan 6. PMID: 33410157
Shu D, Young JG, Toh S, Wang R. Variance estimation in inverse probability weighted Cox models. Biometrics. 2020 Jul; Epub ahead of print. PMID: 32662087
Shu D, Yoshida K, Fireman BH, Toh S. Inverse probability weighted Cox model in multi-site studies without sharing individual-level data. Stat Methods Med Res. 2020 Jun; 29(6):1668-1681. Epub 2019 Aug 26. PMID: 31448681
Shu D, Yi GY. Causal inference with measurement error in outcomes: Bias analysis and estimation methods. Stat Methods Med Res. 2019 Jul; 28(7):2049-2068. Epub 2017 Dec 15. PMID: 29241426.
Shu D, Yi GY. Weighted causal inference methods with mismeasured covariates and misclassified outcomes. Stat Med. 2019 May; 38(10):1835-1854. Epub 2019 Jan 4. PMID: 30609095