Susan Shortreed'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 GHRI work, 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.
Analysis of complex longitudinal data and data collected from electronic health records; methods for overcoming missing data; computational statistics and algorithms; variable selection methods
Biostatistics; data mining
Biostatistics; treatment for chronic depression and bipolar disorder; suicide prevention; developing personalized dynamic treatment strategies
Thielke S, Shortreed SM, Saunders K, Turner JA, LeResche L, von Korff M. A prospective study of predictors of long-term opioid use among patients with chronic non-cancer pain. Clin J Pain. 2016 Jul 15. [Epub ahead of print]. PubMed
Rossom RC, Shortreed S, Coleman KJ, Beck A, Waitzfelder BE, Stewart C, Ahmedani BK, Zeber JE, Simon GE. Antidepressant adherence across diverse populations and healthcare settings. Depress Anxiety. 2016 Jun 20. doi: 10.1002/da.22532. [Epub ahead of print].
Turner JA, Shortreed SM, Saunders KW, LeResche L, Von Korff M. Association of levels of opioid use with pain and activity interference among patients initiating chronic opioid therapy: a longitudinal study. Pain. 2016 Apr;157(4):849-57. doi: 10.1097/j.pain.0000000000000452. PubMed
Simon GE, Coleman KC, Rossom R, Beck A, Oliver M, Johnson E, Whiteside U, Operskalski B, Penfold RB, Shortreed SM, Rutter C. Risk of suicide attempt and suicide death following completion of the patient health questionnaire depression module in community practice. J Clin Psychiatry. 2016 Feb;77(2):221-7. doi: 10.4088/JCP.15m09776. PubMed
Mental health research excels at linking bad experiences to poor outcomes, writes Dr. Greg Simon. Here’s how to focus on recovery and resilience instead.
Read about it in Healthy Findings.