Professor, Biostatistics Program
Public Health Sciences Division, Fred Hutch
Hutchinson Institute for Cancer Outcomes Research (HICOR), Fred Hutch
Dr. Carolyn Rutter is a professor with both the Hutchinson Institute for Cancer Outcomes Research (HICOR) and the Biostatistics Program within the Public Health Sciences Division. Her research interests include microsimulation modeling and model calibration, evaluation of diagnostic and screening tests, and meta-analysis and systematic reviews, with a focus on Bayesian approaches. She is interested in understanding and addressing health care inequities and assessing health care quality and provider performance. Dr. Rutter is a principal investigator for a Cancer Intervention and Surveillance Modeling Network (CISNET) team focused on colorectal cancer and led the development of the CRC-SPIN microsimulation model. Her CISNET work focuses on reducing the population burden of colorectal cancer by comparing the effectiveness of policies for CRC control. She is a fellow of the American Statistical Association and has published more than 150 articles in scientific journals. Dr. Rutter served as an affiliate member of Fred Hutch from 2012-2014. Previous to rejoining the Hutch, she worked as an investigator with Group Health Research Institute (now Kaiser Permanente Washington Health Research Institute), the UW Biostatistics & Health Services departments and RAND Corporation.
University of California, Los Angeles, 1991, Ph.D. (Biostatistics)
University of California, Los Angeles, 1988, M.S. (Biostatistics)
University of California, Los Angeles, 1986, B.S. (Applied Mathematics)
Developing and applying Bayesian models
Evaluation of diagnostic and screening tests
Meta-analysis and systematic reviews
Understanding and addressing health care inequities
Assessing health care quality and provider performance
Dr. Rutter is a principal investigator (PI) for a Cancer Intervention and Surveillance Modeling Network (CISNET) team and led the development of the CRC-SPIN microsimulation model for colorectal cancer. She developed a likelihood-free method for model calibration, implemented using the R package “imabc”. This method is especially useful for complex models that are calibrated to multiple targets.