I am an Assistant Professor in Biostatistics at the Johns Hopkins School of Public Health. My research interests include adaptive clinical trial designs, robustness to model misspecification, causal inference, and HIV/AIDS prevention and treatment.
Postdoc Positions Available
I have two postdoc positions available. One is in causal inference with applications in HIV prevention (Pre-Exposure Prophylaxis); the other is in adaptive randomized trial designs. Please email me your CV if you are interested to discuss these.
- Rosenblum, M. (in press) Adaptive Randomized Trial Designs that Cannot Be Dominated By Any Standard Design at the Same Total Sample Size. Biometrika. [LINK]
- Rosenblum, M, Liu H, Yen E-H. (in press) “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). [LINK]
Example of Optimal Rejection Regions Produced by Method in Above Paper:
- Rosenblum, M, (in press) “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. [LINK]
Patient Centered Outcomes Research Institute (PCORI) Funding for Methodological Research Project: Innovative Randomized Trial Designs to Generate Stronger Evidence about Subpopulation Benefits and Harms (Principal Investigator: Michael Rosenblum. Duration: 2014 to 2017.)
Papers on Improved Design and Analysis for Randomized Trials
- Rosenblum, M., Fang, X., and Liu, H., (2014) Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming. [Click here for Working Paper] [Click here for slides from recent talk]
- Colantuoni, E. and Rosenblum, M. (2014) Leveraging Prognostic Baseline Variables to Gain Precision in Randomized Trials [Click here for Working Paper (under review at Statistics in Medicine)]
- 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]
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