The Johns Hopkins Institute for Clinical and Translational Research (ICTR) is sponsoring a lunch and learn lecture series to disseminate new methodologies related to clinical trials. The initial lecture, “Improving Precision by Adjusting for Prognostic Baseline Variables in Randomized Trials, Without Regression Model Assumptions,” will be Thursday, May 25 from 11:30 a.m.- 1 p.m. at Tilghman Auditorium in the Miller Research Building. Speaker Michael Rosenblum, PhD, associate professor of biostatistics at the Johns Hopkins Bloomberg School of Public Health, will share how his research develops improved methods for the design and analysis of randomized trials.
Rosenblum will discuss:
Lunch will be provided. To register, visit: https://lunchandlearnmay25.eventbrite.com
For more information, contact Michael Rosenblum at firstname.lastname@example.org.
Michael Rosenblum, PhD
Associate Professor of Biostatistics
Johns Hopkins Bloomberg School of Public Health
Michael Rosenblum is an Associate Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health. He received his Ph.D. in Applied Math from MIT, followed by a postdoc in Biostatistics at the University of California, Berkeley and the Center for AIDS Prevention Studies (CAPS) at the University of California, San Francisco. A major focus of his research is developing improved methods for the design and analysis of randomized trials. Specific goals include (i) developing adaptive trial design methods to determine which subpopulations benefit from and are harmed by different treatments, and (ii) developing open-source, software tools to make new, proven design and analysis methods widely available and easy to implement.
He collaborates with clinical investigators in stroke, HIV prevention, and Alzheimer’s disease prevention. https://mrosenblumbiostat.wordpress.com/
In randomized clinical trials with baseline variables that are correlated with the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables, but little guidance on which is best to use in a given context. The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions. We give practical guidance on how to avoid this problem, while retaining the advantages of covariate adjustment. This can be achieved by using simple (but less well-known) standardization methods from the recent statistics literature. We discuss these methods and give software in R and Stata implementing them. A data example from a recent stroke trial is used to illustrate these methods. This is joint work with Jon Arni Steingrimsson, Elizabeth Colantuoni, and Daniel Hanley.
Some key ideas in the talk are covered in the following 2017 paper in Contemporary Clinical Trials: https://goo.gl/IAFVS8