I am an Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. My research interests include adaptive clinical trial designs, robustness to model misspecification, causal inference, and HIV/AIDS prevention and treatment.

Recent manuscript on statistical methods for COVID-19 treatment trials:

David Benkeser, Ivan Diaz, Alex Luedtke, Jodi Segal, Daniel Scharfstein, Michael Rosenblum (In Press) Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Ordinal or Time to Event Outcomes. Biometrics (Practice Section). This paper was selected to be a discussion paper. medRxiv version

2020 ASA Biopharmaceutical Section Statistics Workshop Slides on topic above.

JSM 2020 Slides and Presentation (Video) on topic above.

Click Here for Open-Source Software Tool for Planning/Optimizing Adaptive Enrichment Trial Designs, Video Recording of Short-Course Taught at FDA on This, and Statistical Methods for Improving Precision and Power in Randomized Trials by Adjusting for Prognostic Baseline Variables

Selected Publications (Click here for CV).

* Indicates primary mentorship of trainee on this manuscript

  • Rosenblum, M., Fang, X., and Liu, H. (In Press) Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming. Journal of the Royal Statistical Society, Series B. Working paper version
  • 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.
  • Hanley, D. F., Thompson, R. E., Rosenblum, M., Yenokyan, G., Lane K., McBee, N., Mayo S. W., Bistran-Hall, A. J., Gandhi, D. Mould, W. A., Ullman, N., Ali, H., Carhuapoma, J. R. Kase, C. S., Lees, K. R., Dawson, J., Wilson, A., Betz, J. F., Sugar, E., Hao, Y., Avadhani, R., Caron, J.-L., Harrigan, M. R., Carlson, A. P., Bulters, D., LeDoux, D. E., Huang, J., Cobb, C., Gupta, G., Kitagawa, R., Chicoine, M. R., Patel, H., Dodd, R., Camarata, P. J., Wolfe, S., Stadnik, A., Money, P. L., Mitchell, P., Sarabia, R., Harnof, S., Barzo, P., Unterberg, A., Teitelbaum, J. S., Wang, W., Anderson, C. S., Mendelow, A. D., Gregson, B., Janis, S., Vespa, P., Ziai, W., Zuccarello, M., Awad, I. A., for the MISTIE III Investigators. (2019) Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial. The Lancet. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(19)30195-3
  • *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
  • Wu, A.W., Weston, C.M., Chidinma, A.I., Ruberman, C., Bone, L., Boonyasai, R., Hwang, S., Gentry, J., Purnell, L., Lu, Yanyan, Liang, S., and Rosenblum, M. The Baltimore CONNECT (Community-based Organizations Neighborhood Network: Enhancing Capacity Together) Cluster Randomized Controlled Trial. (2019) American Journal of Preventive Medicine. https://doi.org/10.1016/j.amepre.2019.03.013
  • *Huang, E. J., Fang, E. X., Hanley, D. F., and Rosenblum, M. (2019) Constructing a Confidence Interval for the Fraction Who Benefit from Treatment, Using Randomized Trial Data. Biometrics. https://doi.org/10.1111/biom.13101
  • Steingrimsson, J.A., Betz, J., Qian, T., and Rosenblum, M. (2019) Optimized Adaptive Enrichment Designs for Three-Arm Trials: Learning which Subpopulations Benefit from Different Treatments. Biostatistics. https://doi.org/10.1093/biostatistics/kxz030
  • *Fisher, A., Rosenblum, M. & for the Alzheimer’s Disease Neuroimaging Initiative (2018) Stochastic Optimization of Adaptive Enrichment Designs for Two Subpopulations, Journal of Biopharmaceutical Statistics, 28(5), 966-982, Open Access link to Journal Version
  • Dıaz, I., Colantuoni, E., Hanley, D. F., and Rosenblum, M. (2018) Improved Precision in the Analysis of Randomized Trials with Survival Outcomes, without Assuming Proportional Hazards. Lifetime Data Analysis. PDF
  • Rosenblum, M., and Hanley, D.F. (2017) Topical Review: Adaptive Enrichment Designs for Stroke Clinical Trials. Stroke. 48(6). LINK TO PAPER
  • Steingrimsson, J. A., Hanley, D. F., and Rosenblum, M., (2017) Improving Precision by Adjusting For Baseline Variables in Randomized Trials with Binary Outcomes, without Regression Model Assumptions. Contemporary Clinical Trials. PDF Version
  • *Huang, E., Fang, E., Hanley, D., and Rosenblum, M., (2017) Inequality In Treatment Benefits: Can We Determine If a New Treatment Benefits the Many or the Few? Biostatistics. PDF Version
  • Rosenblum, M., Qian, T., Du, Y., and Qiu, H., Fisher, A. (2016) Multiple Testing Procedures for Adaptive Enrichment Designs: Combining Group Sequential and Reallocation Approaches. Biostatistics. 17(4), 650-662. PDF version
  • Rosenblum, M., Thompson, R., Luber, B., Hanley, D. (2016) Group Sequential Designs with Prospectively Planned Rules for Subpopulation Enrichment. Statistics in Medicine. 35(21), 3776-3791. http://goo.gl/7nHAVn
  • Patil, P., Colantuoni, E., Leek, J. T., Rosenblum, M. (2016) Measuring the Contribution of Genomic Predictors to Improving Estimator Precision in Randomized trials. Contemporary Clinical Trials Communications. 48-54. http://dx.doi.org/10.1016/j.conctc.2016.03.001
  • Diaz, I., Colantuoni, E., Rosenblum, M. (2016) Enhanced Precision in the Analysis of Randomized Trials with Ordinal Outcomes. Biometrics. (72) 422-431 [LINK]
  • Rosenblum, M. (2015) Adaptive Randomized Trial Designs that Cannot Be Dominated By Any Standard Design at the Same Total Sample Size. 102(1). 191-202. Biometrika. [LINK]
  • Rosenblum, M, Liu H, Yen E-H. (2014) “Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, Using Sparse Linear Programming” Journal of the American Statistical Association (Theory and Methods). Volume 109. Issue 507. 1216-1228. [LINK]

Grant Funding: I am honored to be a recipient of the 2017 Burroughs Wellcome Fund (BWF) Innovation in Regulatory Science Awards: “BWF’s Innovation in Regulatory Science Awards provide up to $500,000 over five years to academic investigators developing new methodologies or innovative approaches in regulatory science that will ultimately inform the regulatory decisions the Food and Drug Administration (FDA) and others make.”

Chili Contest 2014

Causal Inference Working Group, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health


mrosen “–at–” jhu–dot–(dashes and this phrase inserted to avoid spam) edu