Rebecca Hubbard, PhD

Dr. Rebecca Hubbard's lab, housed in the Department of Biostatistics at the Brown University School of Public Health, focuses on the development, application, and evaluation of statistical methods for the analysis of data derived from electronic health records (EHR). Our objective is to develop new approaches to the design and analysis of studies using EHR data to help make research on health and health care more valid, efficient, and patient-centered.

Our current projects include research on statistical methods for mismeasured and informatively missing covariates derived form the EHR (MERMAID), linking complex EHR data including structured data and neuroimaging with prospectively collected longitudinal cohort data (GARDENIA), and phenotype estimation and analysis for pediatric type II diabetes (PEPPER) which seeks to develop novel methods for estimating and analyzing EHR-derived phenotypes. We collaborate with a wide variety of clinical and epidemiological researchers including investigators in oncology, cardiology, and neurology at Penn as well as collaborators at the Children's Hospital of Philadelphia and Kaiser Permanente Washington Health Research Institute.

Select Publications

1. Zhang H, Clark AS, Hubbard RA. 2024. A quantitative bias analysis approach to informative presence bias in electronic health records. Epidemiology. 35(3):349-358.
2. Yuan C, Linn KA, Hubbard RA. 2023. Algorithmic fairness of machine learning models for Alzheimer's Disease progression. JAMA Network Open. 6(11): e2342203.
3. Hubbard RA, Pujol TA, Alhajjar E, Edoh K, Martin ML. 2023. Identifying sources of disparities in surveillance mammography performance and personalized recommendations for supplemental breast imaging: A simulation study. Cancer, Epidemiology, Biomarkers & Prevention. 32 (11): 1531–1541.
4. Vader DY, Mamtani R, Li Y, Griffith SD, Calip GS, Hubbard RA. 2023. Inverse probability of treatment weighting and missingness in confounder data in EHR-based analyses: a comparison of three missing data approaches using plasmode simulation. Epidemiology. 34(4): 520–530.
5. Getz K, Hubbard RA, Linn K. 2023. Performance of multiple imputation using modern machine learning methods in electronic health records data. Epidemiology. 34(2):206-215.
6. Su Y-R, Buist DSM, Lee JM, Ichikawa L, Miglioretti D, Bowles E, Wernli KJ, Kerlikowske K, Tosteson A, Lowry KP, Henderson L, Sprague B, Hubbard RA. 2023. Performance of statistical and machine learning risk prediction models for breast cancer surveillance benefits and failures. Cancer Epidemiology, Biomarkers & Prevention. 32(4):561-571.
7. Harton J, Segal B, Mamtani R, Mitra N, Hubbard RA. 2023. Combining real-world and randomized control trial data using data-adaptive weighting via the on-trial score. Statistics in Biopharmaceutical Research. 15(2):408-420.
8. Harton J, Mitra N, Hubbard RA. 2022. Informative presence bias in analyses of electronic health records-derived data: A cautionary note. Journal of the American Medical Informatics Association. 29(7):1191-9.
9. Getz K, Mamtani R, Hubbard RA. 2021. Integrating real world data and clinical trial results using survival data reconstruction and marginal moment-balancing weights. Journal of Biopharmaceutical Statistics. 32(1):191-203.
10. Hubbard RA, Lett E, Ho G, Chubak J. 2021. Characterizing bias due to differential exposure ascertainment in electronic health record data. Health Services and Outcomes Research Methodology. 21:309-323.
11. Hubbard RA, Xu J, Chen Y, Siegel R, Eneli I. 2020. Studying pediatric health outcomes with electronic health records using Bayesian clustering and trajectory analysis. Journal of Biomedical Informatics. 113:103654.