Center & Institute Affiliations
Michael Rosenblum, PhD, MS, develops innovative clinical trial designs and open-source software to efficiently learn which treatments work best for which populations.
Experiences & Accomplishments
Massachusetts Institute of Technology
My research interests include causal inference, adaptive clinical trial designs, robustness to model misspecification, and HIV/AIDS prevention and treatment.
- Wang, B., Susukida, R., Mojtabai, R., Amin-Esmaeili, M., and Rosenblum, M. (2021) Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Additional Baseline Variables. Journal of the American Statistical Association, Theory and Methods Section. https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1981338
- David Benkeser, Ivan Diaz, Alex Luedtke, Jodi Segal, Daniel Scharfstein, Michael Rosenblum (2021) Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Ordinal or Time to Event Outcomes. Biometrics (Practice Section). https://doi.org/10.1111/biom.13377 This paper was selected to be a discussion paper. Discussion by Dr. Lisa LaVange: https://onlinelibrary.wiley.com/doi/full/10.1111/biom.13494
- Rosenblum, M., Fang, X., and Liu, H. (2020) Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming. Journal of the Royal Statistical Society, Series B. 82, 749-772. http://doi.org/10.1111/rssb.12366
- Rosenblum, M., Miller, P., Reist, B., Stuart, E., Thieme, M., and Louis, T. (2019) Adaptive Design in Surveys and Clinical Trials: Similarities, Differences, and Opportunities for Cross-Fertilization. Journal of the Royal Statistical Society, Series A (Statistics in Society). 182, 963-982. https://doi.org/10.1111/rssa.12438 Article selected for presentation at 2019 Royal Statistical Society International Conference.
- Wang, B., Ogburn, E., and Rosenblum, M. (2019) Analysis of Covariance (ANCOVA) in Randomized Trials: More Precision and Valid Confidence Intervals, Without Model Assumptions. Biometrics. https://doi.org/10.1111/biom.13062