Yates Coley, PhD, is a biostatistician whose research promotes predictive analytics and learning health systems as a way to improve value and quality in health care delivery. Her statistical research focuses on developing clinical prediction models that are accurate, actionable, and fair. This work spans several statistical domains including repeated measurements, missing data, and machine learning.
Dr. Coley is currently a scholar with the CATALyST K12 Washington Learning Health System Program funded by the Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute. As part of her training in learning health system research, Dr. Coley is studying current barriers to implementing evidence-based predictive analytics tools to help develop prediction tools that can be deployed and sustained in clinical care. Her research plan also focuses on statistical methods to address racial bias in clinical prediction algorithms.
Before starting as an assistant investigator at Kaiser Permanente Washington Health Research Institute (KPWHRI) in 2016, Dr. Coley was a postdoctoral research fellow at Johns Hopkins Bloomberg School of Public Health. There, she worked with urologists to develop a prediction model that enables personalized management of low-risk prostate cancer. She designed an interactive decision support tool that calculates and communicates patients’ predictions in real-time and is currently building the statistical structure necessary to support a continuously learning model. The resulting prediction tool will be integrated into the clinical workflow so that new observations will be automatically incorporated into the existing model, improving both patient-level predictions as well as researchers’ understanding of risk in the population.
Dr. Coley completed her PhD in biostatistics at the University of Washington. Her dissertation research proposed methods to improve effectiveness estimates in HIV prevention trials by accounting for unobserved variability in risk.
At KPWHRI, Dr. Coley collaborates on projects across a range of research areas including mental health, breast cancer imaging, bariatric surgery, and health services.
Bayesian analysis, causal inference, data visualization, hierarchical models, longitudinal data analysis, missing data, prediction, survival analysis
Suicide risk, depression treatment, measurement-based care, antipsychotic use in adolescents
Biostatistics, prostate cancer, risk stratification, stakeholder engagement, surveillance
Biostatistics, data visualization, interactive decision-support tools, learning health systems, stakeholder engagement
Biostatistics, clinical decision-support, learning health systems, patient-centeredness, shared decision-making, stakeholder engagement
Courcoulas AP, Coley RY, Arterburn D. Evidence-based and patient-centered decisions regarding bariatric surgery-reply. JAMA Surg. 2020 Jul 1. doi: 10.1001/jamasurg.2020.1530. [Epub ahead of print]. PubMed
Lowry KP, Coley RY, Miglioretti DL, Kerlikowske K, Henderson LM, Onega T, Sprague BL, Lee JM, Herschorn S, Tosteson ANA, Rauscher G, Lee CI. Screening performance of digital breast tomosynthesis vs digital mammography in community practice by patient age, screening round, and breast density. JAMA Netw Open. 2020 Jul 1;3(7):e2011792. doi: 10.1001/jamanetworkopen.2020.11792. PubMed
Coley RY, Boggs JM, Simon GE. Measuring outcome of depression: it is complicated. Psychiatr Serv. 2020;71(5):528. doi: 10.1176/appi.ps.71502. PubMed
McTigue KM, Wellman R, Nauman E, Anau J, Coley RY, Odor A, Tice J, Coleman KJ, Courcoulas A, Pardee RE, Toh S, Janning CD, Williams N, Cook A, Sturtevant JL, Horgan C, Arterburn D. Comparing the 5-year diabetes outcomes of sleeve gastrectomy and gastric bypass: the National Patient-Centered Clinical Research Network (PCORNet) - Bariatric Study. JAMA Surg. 2020 Mar 4:e200087. doi: 10.1001/jamasurg.2020.0087. [Epub ahead of print]. PubMed
Sprague BL, Coley RY, Kerlikowske K, Rauscher GH, Henderson LM, Onega T, Lee CI, Herschorn SD, Tosteson ANA, Miglioretti DL. Assessment of radiologist performance in breast cancer screening using digital breast tomosynthesis vs digital mammography. JAMA Netw Open. 2020;3(3):e201759. doi: 10.1001/jamanetworkopen.2020.1759. PubMed
But for most women, digital breast tomosynthesis improves cancer detection and reduces recalls.
Watch video on latest results from PCORnet Bariatric Study. (Spoiler alert: Bypass, not sleeve.)
Dr. David Arterburn and colleagues publish a large, long-term analysis of post-op safety of weight-loss surgeries.