Instructions for Accessing Trial Planning Tool for Adaptive Enrichment Designs that Optimizes and Compares Performance of Adaptive Versus Standard Designs
- Video-recording of Short Course Taught at the FDA on 11/20/17: Adaptive Enrichment Trial Designs: Statistical Methods, Trial Optimization Software, and Four Case Studies, Presented by Michael Rosenblum and Josh Betz, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
- Enhanced Precision Estimators for Randomized Trials with Repeated Measures Using Targeted Maximum Likelihood Estimation (R and SAS code)
- Plain Language Document Outlining Advantages and Limitations of Adaptive Enrichment Designs for Confirmatory Randomized Clinical Trials,
Demonstrated using Simulation Studies in Stroke, Cardiac Resynchronization Therapy, HIV Treatment, and Alzheimer's Disease
- Video-Recording of Short-Course on Adaptive Enrichment Clinical Trial Designs (August 30, 2017, Johns Hopkins University, Baltimore, MD)
- Video-Recording of Short-Course on Adaptive Enrichment Clinical Trial Designs (June 13, 2017, University of California Berkeley’s Forum for Collaborative Research, Washington, D.C.)
Featured Project: Improving Estimator Precision and Robustness in Randomized Trials
In randomized clinical trials with baseline variables that are prognostic for 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.
The goal of this project (currently underway) is to give practical guidance on how to avoid this problem, while retaining the advantages of covariate adjustment. We will discuss relevant statistical methods and software (which apply to continuous, binary, and time-to-event outcomes). Data examples from stroke and Alzheimer's disease trials will be used to illustrate these methods.
If you have any questions or would like to apply for an account, please contact mrosen “–at–” jhu–dot–(dashes and this phrase inserted to avoid spam) edu