Dr.(Prof.) Carolyn Rutter

Dr. Carolyn Rutter is a distinguished professor in the Biostatistics Program at Fred Hutchinson Cancer Center, where she contributes groundbreaking research through her roles at the Public Health Sciences Division, the Hutchinson Institute for Cancer Outcomes Research (HICOR), and the Translational Data Science Integrated Research Center (TDS IRC). With over three decades of experience, Dr. Rutter is a national leader in microsimulation modeling, biostatistical methods, and health outcomes research. Her contributions lie at the intersection of applied statistics, public health, and cancer research. Dr. Rutter’s academic background is rooted in biostatistics and applied mathematics, with BS, MS, and PhD degrees from UCLA. She brings to her work a deep command of Bayesian modeling, diagnostic test evaluation, and systematic reviews and meta-analysis. She is widely recognized for her methodological rigor and for applying complex models to real-world healthcare challenges, especially those concerning cancer screening and diagnostic effectiveness. A major component of Dr. Rutter’s research involves microsimulation modeling for colorectal cancer control. As a principal investigator for the Cancer Intervention and Surveillance Modeling Network (CISNET), she led the development of the CRC-SPIN model—a sophisticated microsimulation tool implemented in C# and calibrated using Bayesian methods. Notably, she developed a likelihood-free method for model calibration, operationalized via the R package “imabc,” which enhances the calibration of complex health models to multiple data targets. Dr. Rutter's work with CISNET and the NCI PROSPR Research Center is dedicated to reducing colorectal cancer burden through evidence-based policy evaluation and prediction. She leads teams using diverse data sources, including electronic health records, natural language processing (NLP) of free-text clinical notes, and structured EMR components like EPIC smartphrases. Her current efforts focus on refining predictive models, advancing personalized screening regimens, and enhancing healthcare delivery through rigorous statistical assessment. Previously, Dr. Rutter held leadership roles at RAND Corporation and spent 20 years at Group Health Cooperative (now Kaiser Permanente Washington Health Research Institute), where she also served as affiliate faculty in the University of Washington’s Department of Biostatistics. She has authored over 150 peer-reviewed publications and is a fellow of the American Statistical Association, an honor reflecting her influence on statistical practice in healthcare research. Dr. Rutter’s research addresses not only methodological advancements but also broader social challenges, such as healthcare quality, performance metrics, and equity in care delivery. Her work is instrumental in shaping how policymakers and providers evaluate and improve cancer screening protocols and healthcare system performance. Through her interdisciplinary and applied approach, Dr. Rutter exemplifies how sophisticated biostatistical tools can transform public health by guiding more equitable and effective clinical decisions. Her work continues to inform national cancer screening guidelines and support large-scale health systems in delivering better patient outcomes.

• Microsimulation modeling
• Bayesian analysis
• Biostatistics
• Model calibration
• Diagnostic test evaluation
• Meta-analysis techniques
• Systematic review methodology
• R programming
• “imabc” R package development
• C# programming
• Natural language processing
• EMR data extraction
• Predictive modeling
• Healthcare data integration
• Statistical programming
• Research project leadership
• Comparative effectiveness analysis
• Data synthesis
• Cancer screening optimization
• Health policy analysis
• Interdisciplinary collaboration
• Healthcare performance evaluation
• Statistical mentoring
• Scientific writing
• Epidemiological modeling
• Longitudinal data analysis
• Multisite data coordination
• Cancer risk modeling
• Population health analytics
• Evidence-based policy evaluation
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