Susan Shortreed, PhD, uses statistics and machine learning methods to address health science problems, with a special emphasis on analyzing complex longitudinal data. She develops and evaluates statistical approaches for observational data, and works to improve the design and analyses of studies that use data collected from electronic health care records. She is leading a project to develop statistical methods for constructing personalized treatment strategies using data captured from electronic health records.
Dr. Shortreed earned her PhD in statistics from the University of Washington. Then 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 in Montreal, Canada. Dr. Shortreed has collaborated with scientists in a broad range of areas including alcohol use, cancer screening, and medication safety. She now works alongside researchers in mental and behavioral health, evaluating and comparing treatments for chronic pain and depression, and interventions to prevent 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 and developing risk prediction algorithms to identify individuals who may be at increased risk for suicidal behavior.
Dr. Shortreed is also an affiliate associate professor of biostatistics at the University of Washington School of Public Health. She served 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.
Design and analysis of studies that use data collected from electronic health records; analysis of complex longitudinal data; methods for constructing personalized treatment strategies, computational statistics and algorithms; machine learning; variable selection methods.
Biostatistics; machine learning; using data collected from electronic health records to study rare adverse events; opioid safety; medication safety in pregnancy.
Biostatistics; treatment for chronic depression; suicide prevention; developing personalized treatment strategies; developing risk prediction models.
Shortreed S, Forbes A. Inverse probability weighted estimation of the marginal odds ratio: correspondence regarding `The performance of different propensity score methods for estimating marginal odds ratios.' Stat Med. 2008 Nov 20;27(26):5556-9;
Shortreed SM, Peeters P, Forbes A. Estimating the reduction in cardiovascular disease mortality risk. Abstract in Proceedings of the American Heart Association Scientific Sessions, 2008. Oral presentation at American Heart Association Scientific Sessions Conference, 11/08.
Shortreed S, Handcock M, Hoff P. Positional estimation within the latent space model for networks. Methodology. 2006;2(1):24-33.
Shortreed S, Meila M. Unsupervised spectral learning. Proceedings of The 21st Conference in Uncertainty in Artificial Intelligence. Fahiem Bacchus and Tommi Jaakkola, eds. 2005.
Meila M, Shortreed S, Liang X. Regularized spectral learning. Proceedings of the Conference in Artificial Intelligence and Statistics. Edited by Robert Cowell and Zoubin Ghahramani. 2005.
A study led by Dr. Sascha Dublin finds similar outcomes for 3 hypertension medications, filling an evidence gap.
New work by Susan Shortreed, PhD, finds infection risks drive worse outcomes for some racial and ethnic groups.
Dr. Sascha Dublin tells how studies of KP electronic health record data can improve COVID-19 treatment and prevention.
The honor underscores the institute’s commitment to a work environment that fosters employees’ growth.
Healio Psychiatry, April 12, 2023