Rebecca Hubbard, PhD

“I want people to know what to expect from cancer screening so they can work with their doctors to make informed choices that fit with their risk factors and preferences.” 

Rebecca Hubbard, PhD

Group Health Research Institute Associate Investigator

Biography

Associate Investigator and Biostatistician Rebecca Hubbard leads innovative research to develop statistical approaches for longitudinal observational studies of cancer screening that help people make more informed decisions. Her work addresses important questions about how best to balance benefits and harms of alternative strategies for cancer screening and detection.

Since joining GHRI in 2008, Dr. Hubbard has played an integral role in the Statistical Coordinating Center (SCC) of the Breast Cancer Surveillance Consortium (BCSC)—a National Cancer Institute (NCI)-funded network of mammography registries that serves as the nation’s largest longitudinal collection of breast imaging data. She is the principal investigator of the BCSC Data Resource, which provides data and analytic support for researchers worldwide who use BCSC data to answer important questions about breast cancer screening. She also leads all SCC projects that use Medicare data. Collectively, these projects are helping researchers, policymakers, providers, and patients understand the relationship between cancer screening and health care utilization more broadly.

At a time when the nation is grappling with questions about best practices for cancer screening—especially for breast cancer—Dr. Hubbard is also developing new statistical methods for estimating the long-term implications of repeat cancer screening. She used this novel approach in a 2011 breast cancer study that examined false-positive results from screening mammograms for 169,000 women in the BCSC database. Her team found that, on average, more than half of women screened yearly for a decade would have a false-positive result—and that having prior mammograms for comparison cut the odds of a false-positive result in half. Published in the Annals of Internal Medicine (see news release), Dr. Hubbard’s paper was recognized as one of the top 12 publications of 2011 funded by NCI’s Epidemiology and Genomics Research Program.

Among Dr. Hubbard’s other statistical interests are hierarchical models, Bayesian methods, and multistate models including Markov processes. Her other areas of application include colorectal cancer and aging and dementia. Dr. Hubbard serves as a journal referee and belongs to professional organizations including the International Biometric Society and the American Statistical Association (ASA). In 2008, the ASA’s biometrics section gave her the David P. Byar Young Investigator Award. Dr. Hubbard also has a master’s degree in epidemiology and serves as an affiliate assistant professor in biostatistics at the University of Washington.

Research interests and experience

  • Biostatistics

    Methods for multistate disease processes; hierarchical models; Bayesian methods; methods for longitudinal observational studies

  • Cancer

    Biostatistics; breast cancer; colorectal cancer

  • Aging & Geriatrics

    Biostatistics; Alzheimer’s disease; healthy aging

  • Health Services & Economics

    Biostatistics; evaluation of screening programs; health outcomes research 

Recent publications

Henrikson NB, Anderson ML, Hubbard RA, Fishman P, Grossman DC.

Employee knowledge of value-based insurance design benefits.

Am J Prev Med. 2014;47(2):115-22. doi: 10.1016/j.amepre.2014.03.005. Epub 2014 Jun 17. PubMed

Tom SE, Hubbard RA, Crane PK, Haneuse SJ, Bowen J, McCormick WC, McCurry S, Larson EB.

Characterization of dementia and Alzheimer's disease in an older population: updated incidence and life expectancy with and without dementia.

Am J Public Health. 2014 Jul 17:e1-e6 [Epub ahead of print]. PubMed

Kerlikowske K, Hubbard R, Tosteson AN.

Higher mammography screening costs without appreciable clinical benefit: the case of digital mammography.

J Natl Cancer Inst. 2014 Jul 16;106(8). pii: dju191. doi: 10.1093/jnci/dju191. Print 2014 Aug.

Hubbard RA, Zhu W, Balch S, Onega T, Fenton JJ.

Identification of abnormal screening mammogram interpretation using Medicare claims data.

Health Serv Res. 2014 Jun 28. doi: 10.1111/1475-6773.12194 [Epub ahead of print]. PubMed