Susan Shortreed, PhD's research brings together statistics and machine learning methods to address health science problems, with a special emphasis on analyzing complex longitudinal data and overcoming missing-data challenges. Much of her methodological work is focused on developing and evaluating statistical inference approaches for observational data, such as data from electronic health care records or from randomized clinical trials with missing information. Dr. Shortreed is also interested in developing new machine learning methods and extending current best-practice methods, specifically for personalized dynamic treatment strategies, clustering, and model selection methods.

Dr. Shortreed earned her PhD in statistics from the University of Washington in 2006. After completing her degree, she spent two years in the Department of Epidemiology and Preventive Medicine at Monash University in Melbourne, Australia, and two years in the School of Computer Science at McGill University. Dr. Shortreed has collaborated with scientists in a broad range of areas including cancer screening, cardiovascular disease, and medication and vaccine safety. Currently, she works most often with researchers in mental and behavioral health, evaluating and comparing treatments for chronic pain, depression, and bipolar disorder, and interventions to prevent alcohol misuse, smoking, and suicide. Dr. Shortreed is an investigator with the Mental Health Research Network, designing studies to address important public health concerns, such as determining which antidepressant medications work best for which patients.

In addition to her work at Kaiser Permanente Washington Health Research Institute, Dr. Shortreed is an affiliate associate professor at the University of Washington Biostatistics Department. She serves on the executive board for the American Statistical Association’s Section on Statistics in Epidemiology and the editorial board of the Journal of the Royal Statistical Society, Series C: Applied Statistics.