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 assistant 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
Friesen MC, Shortreed SM, Wheeler DC, Burstyn I, Vermeulen R, Pronk A, Colt JS, Baris D, Karagas MR, Schwenn M, Johnson A, Armenti KR, Silverman DT, Yu K.Using hierarchical cluster models to systematically identify groups of jobs with similar occupational questionnaire response patterns to assist rule-based expert exposure assessment in population-based studies.
Ann Occup Hyg. 2014 Dec 3. pii: meu101 [Epub ahead of print]. PubMed
Turner JA, Saunders K, Shortreed SM, LeResche L, Riddell K, Rapp SE, Von Korff M.Chronic opioid therapy urine drug testing in primary care: prevalence and predictors of aberrant results.
J Gen Intern Med. 2014 Sep 13 [Epub ahead of print]. PubMed
Shortreed SM, Laber E, Scott Stroup T, Pineau J, Murphy SA.A multiple imputation strategy for sequential multiple assignment randomized trials.
Stat Med. 2014 Jun 11. doi: 10.1002/sim.6223 [Epub ahead of print]. PubMed
Friesen MC, Locke SJ, Zaebst D, Viet S, Shortreed S, Chen YC, Koh DH, Pardo L, Schwartz KL, Davis FG, Stewart PA, Colt JS, Purdue MP.0199 Using machine learning to efficiently use multiple experts to assign occupational lead exposure estimates in a case-control study.
Occup Environ Med. 2014 Jun;71 Suppl 1:A25-6. doi: 10.1136/oemed-2014-102362.79. PubMed