Susan Shortreed, PhD

“By using rich data sources such as electronic health records, we can begin to identify which treatments will work best for which people.”

Susan M. Shortreed, PhD

Senior Biostatistics Investigator, Kaiser Permanente Washington Health Research Institute
Affiliate Professor, Department of Biostatistics, University of Washington

Susan.M.Shortreed@kp.org
206-287-2088

Curriculum vitae (CV)

Biography

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 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.

Research interests and experience

Biostatistics

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.

Medication Use & Patient Safety

Biostatistics; machine learning; using data collected from electronic health records to study rare adverse events; opioid safety; medication safety in pregnancy.
Mental Health

Biostatistics; treatment for chronic depression; suicide prevention; developing personalized treatment strategies; developing risk prediction models.

Recent publications

Schulz J, Moodie EEM, Shortreed SM. No unmeasured confounding: Known unknowns or... not? Am J Epidemiol. 2023 Jun 6;kwad133. doi: 10.1093/aje/kwad133. Online ahead of print. PubMed

Bian Z, Moodie EEM, Shortreed SM, Bhatnagar S. Variable selection in regression-based estimation of dynamic treatment regimes. Biometrics. 2023 Jun;79(2):988-999. doi: 10.1111/biom.13608. Epub 2021 Dec 18. PubMed

Rose EJ, Moodie EEM, Shortreed SM. Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes. Biom J. 2023 Apr 5:e2100359. doi: 10.1002/bimj.202100359. [Epub ahead of print]. PubMed

Shortreed SM, Walker RL, Johnson E, Wellman R, Cruz M, Ziebell R, Coley RY, Yaseen ZS, Dharmarajan S, Penfold RB, Ahmedani BK, Rossom RC, Beck A, Boggs JM, Simon GE. Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction. NPJ Digit Med. 2023;6(1):47. doi: 10.1038/s41746-023-00772-4. PubMed

Coulombe J, Moodie EE, Shortreed SM, Renoux C. Estimating individualized treatment rules in longitudinal studies with covariate-driven observation times. Stat Methods Med Res. 2023 May;32(5):868-884. doi: 10.1177/09622802231158733. Epub 2023 Mar 16. PubMed

Floyd JS, Walker RL, Kuntz JL, Shortreed SM, Fortmann SP, Bayliss EA, Harrington LB, Fuller S, Albertson-Junkans LH, Powers JD, Lee MH, Temposky LA, Dublin S. Association between diabetes severity and risks of COVID-19 infection and outcomes. J Gen Intern Med.2023 May;38(6):1484-1492. doi: 10.1007/s11606-023-08076-9. Epub 2023 Feb 16. PubMed

Coley RY, Liao Q, Simon N, Shortreed SM. Empirical evaluation of internal validation methods for prediction in large-scale clinical data with rare-event outcomes: A case study in suicide risk prediction. BMC Med Res Methodol. 2023 Feb 1;23(1):33. doi: 10.1186/s12874-023-01844-5. PubMed

Cheetham TC, Shortreed SM, Avalos LA, Reynolds K, Holt VL, Easterling TR, Portugal C, Zhou H, Neugebauer RS, Bider Z, Idu A, Dublin S. Identifying hypertensive disorders of pregnancy, a comparison of two epidemiologic definitions. Front Cardiovasc Med. 2022 Nov 23;9:1006104. doi: 10.3389/fcvm.2022.1006104. eCollection 2022. PubMed

Cruz M, Shortreed SM, Richards JE, Coley RY, Yarborough BJ, Walker RL, Johnson E, Ahmedani BK, Rossom R, Coleman KJ, Boggs JM, Beck AL, Simon GE. Machine learning prediction of suicide risk does not identify patients without traditional risk factors. J Clin Psychiatry. 2022 Aug 31;83(5):21m14178. doi: 10.4088/JCP.21m14178. PubMed

Simon GE, Shortreed SM, Boggs JM, Clarke GN, Rossom RC, Richards JE, Beck A, Ahmedani BK, Coleman KJ, Bhakta B, Stewart CC, Sterling S, Schoenbaum M, Coley RY, Stone M, Mosholder AD, Yaseen ZS. Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events. J Am Med Inform Assoc. 2022 Aug 26:ocac144. doi: 10.1093/jamia/ocac144. [Epub ahead of print]. PubMed