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.


I am honored to be one of five recipients 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.”

My JSM talk slides (2017): Methods and Open-Source Software for Optimizing Adaptive Enrichment Designs

Selected Publications

  • Dıaz, I., Colantuoni, E., Hanley, D. F., and Rosenblum, M. (In Press) Improved Precision in the Analysis of Randomized Trials with Survival Outcomes, without Assuming Proportional Hazards. Lifetime Data Analysis. PDF
  • Fisher, A. and Rosenblum, M. (In Press) Stochastic Optimization of Adaptive Enrichment Designs for Two Subpopulations. Journal of Biopharmaceutical Statistics LINK TO PAPER
  • 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., Fang, X., and Liu, H. Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming. (Under Review): LINK TO WORKING PAPER
  • 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]

Example of Optimal Rejection Regions Produced by Method in Above Paper:

Example Rejection Regions Based on New Sparse LP Method

  • Rosenblum, M, (2014) “Uniformly Most Powerful Tests for Simultaneously Detecting a Treatment Effect in the Overall Population and at Least One Subpopulation” Journal of Statistical Planning and Inference. Volume 155. 107-116. [LINK]


Papers on Improved Design and Analysis for Randomized Trials

  • Huang, E. Fang, E., Hanley, D., and Rosenblum, M. (2015) Inequality In Treatment Benefits: Can We Determine If a New Treatment Benefits The Many Or The Few? Johns Hopkins University, Dept. of Biostatistics Working Papers. [Working Paper]
  • Rosenblum, M., Qian, T., Du, Y., Qiu, H. (2015) Adaptive Enrichment Designs For Randomized Trials With Delayed Endpoints, Using Locally Efficient Estimators To Improve Precision. Johns Hopkins University, Dept. of Biostatistics Working Papers.  [Working Paper]
  • Papers from half-day Workshop led by Michael Rosenblum on Adaptive Designs at the FDA, from the PACES (Partnership in Applied Comparative Effectiveness Science) Collaboration, June 26, 2013. [LINK]

A JHUBiostat Tweet

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


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