Seattle Symposium Slides
Seattle Symposium Short Course Slides (November 15, 2020)
Michael Rosenblum's homepage

Resources on Adaptive Enrichment Trial Designs

Resources on Analysis Methods for Improving Precision and Power by Adjusting for Baseline Variables in Randomized Trials

News (Nov. 2019): FDA releases Guidance for Industry on Adaptive Clinical Trial Designs for Drugs and Biologics.

This FDA Guidance states on page 4 that "An adaptive design can make it possible to answer broader questions than would normally be feasible with a non-adaptive design. For example, an adaptive enrichment design (section V.C.) may make it possible to demonstrate effectiveness in either a given population of patients or a targeted subgroup of that population, where a non-adaptive alternative might require infeasibly large sample sizes."

Featured Project: Improving Estimator Precision and Robustness in Randomized Trials

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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