R. Yates Coley, PhD

“Learning health systems promise to improve medical decision-making in the era of big data by making up-to-date analyses of patient information and scientific knowledge available to physicians and patients in real time.”

Yates Coley, PhD

Assistant Investigator, Kaiser Permanente Washington Health Research Institute

Biography

Yates Coley, PhD, is a biostatistician whose research promotes precision medicine and learning health systems as a way to improve value and quality in health care delivery. Her statistical research focuses on methods for predicting latent health states, understanding variability in risk, and accounting for missing data. She also has particular interest in data visualization and designing decision support tools to support shared clinical decision-making.

Before starting as an assistant investigator at KPWHRI in 2016, Dr. Coley was a post-doctoral research fellow at Johns Hopkins Bloomberg School of Public Health where she worked with Dr. Scott Zeger. She is a co-investigator on a Patient-Centered Outcomes Research Institute (PCORI) methodology grant: “Bayesian hierarchical methods for the design and analysis of studies to individualize health care” (PI: Zeger). This research promises to establish a solid statistical framework for more intelligent, individualized health care provision. 

While at Johns Hopkins, Dr. Coley also worked with urologists to develop a prediction model that enables personalized management of low-risk prostate cancer. She has 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 in such a way 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 breast cancer imaging, bariatric surgery, and antipsychotic medications.

Research interests and experience

  • Biostatistics

    Bayesian analysis, causal inference, data visualization, hierarchical models, longitudinal data analysis, missing data, prediction, survival analysis

  • Cancer

    Biostatistics, prostate cancer, risk stratification, stakeholder engagement, surveillance

  • Health Informatics

    Biostatistics, data visualization, interactive decision-support tools, learning health systems, stakeholder engagement

  • Health Services & Economics

    Biostatistics, clinical decision-support, learning health systems, patient-centeredness, shared decision-making, stakeholder engagement

Recent publications on Biostatistics

Huntley JH, Coley RY, Carter HB, Radhakrishnan A, Krakow M, Pollack CE. Clinical evaluation of an individualized risk prediction tool for men on active surveillance for prostate cancer. LID - S0090-4295(18)30903-8 [pii] LID - 10.1016/j.urology.2018.08.021 [doi] Urology. 2018 Aug 29. pii: S0090-4295(18)30903-8. doi: 10.1016/j.urology.2018.08.021 [Epub ahead of print] PubMed

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, 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)

 

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