Susan Shortreed, PhD

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“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

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

Simon GE, Shortreed SM, Johnson E, Yaseen ZS, Stone M, Mosholder AD, Ahmedani BK, Coleman KJ, Coley RY, Penfold RB, Toh S. Predicting risk of suicidal behavior from insurance claims data vs. linked data from insurance claims and electronic health records. Pharmacoepidemiol Drug Saf. 2023 Dec 19. doi: 10.1002/pds.5734. [Epub ahead of print]. PubMed

Simon GE, Cruz M, Boggs JM, Beck A, Shortreed SM, Coley RY. Predicting outcomes of antidepressant treatment in community practice settings.  Psychiatr Serv. 2023 Dec 5:appips20230380. doi: 10.1176/appi.ps.20230380. [Epub ahead of print]. PubMed

Moodie EEM, Bian Z, Coulombe J, Lian Y, Yang AY, Shortreed SM. Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms. Biostatistics. 2023 Aug 31:kxad022. doi: 10.1093/biostatistics/kxad022. Online ahead of print. PubMed

Simon GE, Cruz M, Shortreed SM, Sterling SA, Coleman KJ, Ahmedani BK, Yaseen ZS, Mosholder AD. Stability of suicide risk prediction models during changes in health care delivery.  Psychiatr Serv. 2023 Aug 17:0. doi: 10.1176/appi.ps.20230172. [Epub ahead of print]. PubMed

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

 

Research

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New findings on treating hypertension in pregnancy

A study led by Dr. Sascha Dublin finds similar outcomes for 3 hypertension medications, filling an evidence gap.

COVID-19

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Greater infection risks linked to COVID-19 disparities

New work by Susan Shortreed, PhD, finds infection risks drive worse outcomes for some racial and ethnic groups.

Drugs, diabetes, disparities

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Studying COVID-19 risk and outcomes

Dr. Sascha Dublin tells how studies of KP electronic health record data can improve COVID-19 treatment and prevention.

news

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Mentorship awards go to Jane Anau and Dr. Jessica Chubak

The honor underscores the institute’s commitment to a work environment that fosters employees’ growth.

KPWHRI IN THE MEDIA

Simpler models for predicting suicide risk work comparably to more complex ones

Q&A: Simple machine learning model predicts suicide risk well

Healio Psychiatry, April 12, 2023