Yates Coley, PhD, is a biostatistician whose research promotes predictive analytics and learning health systems as a way to improve value quality, and equity in health care delivery. Their 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’s paper examining racial and ethnic inequity in two suicide prediction models was awarded Paper of the Year at the Healthcare Systems Research Network 2021 Annual Conference. The two models performed well for visits by patients who were White, Hispanic, and Asian but did not accurately identify high-risk visits for patients who were Black, American Indian, and Alaskan Native, likely due to persistent structural barriers limiting access to affordable, high-quality, and culturally competent mental health care. The study emphasized the importance of assessing performance within racial and ethnic subgroups of all prediction models before clinical implementation to ensure that prediction models ameliorate, rather than exacerbate, existing health disparities.
Dr. Coley is a recent graduate of 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 their training in learning health system research, Dr. Coley studied current barriers to implementing evidence-based predictive analytics tools to help develop prediction tools that can be deployed and sustained in clinical care. Their research plan also focused 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, they worked with urologists to develop a prediction model that enables personalized management of low-risk prostate cancer.
Dr. Coley completed their PhD in biostatistics at the University of Washington. Their 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, aging, 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
Inge T, Coley RY, Bazzano LA, Xanthakos SA, McTigue K, Arterburn D, Williams N, Wellman R, Coleman KJ, Courcoulas A, Desai NK, Anau J, Pardee R, Toh S, Janning C, Cook A, Sturtevant J, Horgan C, Zebrick AJ, Michalsky M. Comparative effectiveness of bariatric procedures among adolescents: the PCORnet bariatric study. Surg Obes Relat Dis. 2018 Apr 17. doi: 10.1016/j.soard.2018.04.002.
Toh S, Wellman R, Coley RY, Horgan C, Sturtevant J, Moyneur E, Janning C, Pardee R, Coleman KJ, Arterburn D, McTigue K, Anau J, Cook AJ. Combining distributed regression and propensity scores: a doubly privacy-protecting analytic method for multicenter research. Clin Epidemiol. 2018 Nov 27;10:1773-1786. doi: 10.2147/CLEP.S178163. eCollection 2018. PubMed
Toh S, Rasmussen-Torvik LJ, Harmata EE, Pardee R, Saizan R, Malanga E, Sturtevant JL, Horgan CE, Anau J, Janning CD, Wellman RD, Coley RY, Cook AJ, Courcoulas AP, Coleman KJ, Williams NA, McTigue KM, Arterburn D, McClay J for the PCORnet Bariatric Study Collaborative. The national Patient-Centered Clinical Research Network (PCORnet) bariatric study cohort: rationale, methods, and baseline characteristics. JMIR Res Protoc. 2017 Dec 5;6(12):e222. doi: 10.2196/resprot.8323. PubMed
Deng D, Du Y, Ji Z, Rao K, Wu Z, Zhu Y, Coley RY. Predicting survival time for metastatic castration resistant prostate cancer:an iterative imputation approach. F1000Research 2016, 5:2672. doi 10.12688/f1000research.8628.1)
Coley RY, Fisher AJ, Mamawala M, Carter HB, Pienta KJ, Zeger SL. A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer. Biometrics. 2016 Aug 22. doi: 10.1111/biom.12577. [Epub ahead of print]. PubMed
Kaiser Permanente researchers stress need to test how prediction models perform in all racial, ethnic groups.
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.)
VeryWell Health, May 6, 2021