Three faculty members in the department of biostatistics, epidemiology and informatics in the Perelman School of Medicine have been named fellows of the American Statistical Association (ASA), the field’s largest and most prestigious professional organization in the US.

Jinbo Chen, a professor of biostatistics, was honored by the ASA for developing innovative statistical methods with cutting edge public health applications; for outstanding scientific collaborations; for exceptional mentoring; and for generous service to the community. Her research has focused on efficient design and analytical methods for biomedical studies that involve complex outcome dependent sampling; risk prediction and risk model evaluation towards precision medicine; statistical methods for genetic epidemiology; and, recently, analysis of electronic health record (EHR) data. Dr. Chen’s methods research has been largely application driven, motivated by her collaborative projects on breast cancer risk prediction, breast imaging biomarker evaluation, maternal and child health, and cardiovascular health studies using Penn Medicine and Veteran Affairs EHRs.

Rebecca Hubbard, an associate professor of biostatistics, was honored by the ASA for her contributions to the analysis of electronic health records and study of cancer epidemiology and service to the society as a leader of the Biometrics section—ASA’s largest section for biostatisticians. Her research focuses on the development and application of statistical methodology for studies that use observational data from clinical medical practice. This work encompasses evaluation of screening and diagnostic test performance, methods for comparative-effectiveness studies, and health-services research.

Nandita Mitra, a professor of biostatistics, vice chair of faculty professional development, chair of the graduate group in biostatistics and epidemiology, and co-director of the Center for Causal Inference, was honored for the development of statistical methods for cost and cost-effectiveness estimation from observational data and for developing innovative causal methods for cancer comparative effectiveness studies. She was also lauded for her dedicated service and leadership on statistical societies, editorial boards, and NIH/NSF study sections.