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
Ertefaie A, Shortreed S, Chakraborty B. Q-learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia. Stat Med. 2016 Jan 10. doi: 10.1002/sim.6859. [Epub ahead of print]. PubMed
Shortreed SM, Laber E, Pineau J, Murphy SA. Imputing missing data from sequential multiple assignment randomized trials. In: Moodie EEM, Kosorok MR, editors. Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine. Philadelphia: SIAM; 2015. p.178-230.
Simon GE, Rossom RC, Beck A, Waitzfelder BE, Coleman KJ, Stewart C, Operskalski B, Penfold RB, Shortreed SM. Antidepressants are not overprescribed for mild depression. J Clin Psychiatry. 2015 Oct 27. [Epub ahead of print]. PubMed
Balderson BH, McCurry SM, Vitiello MV, Shortreed SM, Rybarczyk BD, Keefe FJ, Von Korff M. Information without implementation: a practical example for developing a best practice education control group. Behav Sleep Med. 2015 Oct 20:1-14.[Epub ahead of print]. PubMed