Speeding-up Clinical Trials of COVID-19 Treatments Through Better Data Analysis

Speeding-up Clinical Trials of COVID-19 Treatments Through Better Data Analysis.

There is potential to speed up randomized COVID-19 treatment trials through relatively simple adjustments in how the trial data are analyzed. This session will explain how this can be done, how to apply it to COVID-19 trials as well as trials in other disease areas, and where to find open-source, free software implementing these methods.  This work was recently published in Biometrics.  Join us to learn of upcoming plans for training clinical trials statisticians, including those at JHU and those in low and middle income countries, in how to use these methods in their trials.
 

Michael Rosenblum is an Associate Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health. His research is in causal inference and focuses on developing new statistical methods and software for the design and analysis of randomized trials, with clinical applications in HIV, Alzheimer’s disease, stroke, and cardiac resynchronization devices. He received a 2017 Burroughs Wellcome Fund (BWF) Innovation in Regulatory Science Award that provides funding for his project to develop new methods and software to characterize how robust a proposed design is to violations of an investigator’s assumptions. He is also part of the Johns Hopkins University Center for Excellence in Regulatory Science and Innovation (CERSI).