The Adaptive Enrichment Designs for Confirmatory Randomized Trials: Statistical Methods and Software Tools Workshop occurred Tuesday, June 13, 2017, in Washington D.C.
Instructors:
Michael Rosenblum, PhD
Associate Professor of Biostatistics
Johns Hopkins Bloomberg School of Public Health
Joshua Betz, MS
Research Associate, Department of Biostatistics
Johns Hopkins Bloomberg School of Public Health
Materials:
- Course Syllabus
- Video Recording (available for download at Dr. Rosenblum's website)
Background:
Most randomized trials are designed to determine average treatment effects for a population. This results in trials that may fail to detect important differences in benefits and harms for subpopulations. For example, standard trial designs are not targeted to determine whether a treatment benefits most patients, benefits only a select few, or benefits some patients and harms others. The impact is that treatment recommendations based on the results of standard trial designs may be suboptimal, leading to poor patient outcomes and wasting healthcare resources. This problem affects virtually all disease areas, since it stems from how randomized trials, the gold standard for evaluating treatments, are currently designed and analyzed.
Randomized trial designs that adaptively change enrollment criteria during a trial, called adaptive enrichment designs, have potential to provide improved information about which subpopulations benefit from new treatments. These trial designs involve multiple populations of interest, e.g., defined in terms of a biomarker or risk score measured at baseline.
Course Description:
This course presents an overview of the strengths and limitations of these designs, explains recent advances in statistical methods for these designs, and presents a software tool for optimizing these designs. Two case studies are presented in the context of treatments for stroke and Alzheimer’s disease.
Learning Objectives:
- Understand benefits and limitations of adaptive enrichment designs
- Learn about a new user-friendly, free, open-source software tool that automatically searches over certain adaptive and non-adaptive trial designs to find the ones that best address a clinical investigator’s scientific goals and resource constraints.
Syllabus:
- Part I: Adaptive Enrichment Designs for Confirmatory Randomized Trials: Statistical Methods and Software Tools
Michael Rosenblum & Joshua Betz, Johns Hopkins Bloomberg School of Public Health
Slides - Part II: Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, Using Sparse Linear Programming
Michael Rosenblum, Johns Hopkins Bloomberg School of Public Health
Slides - Part III: Optimization of Adaptive Enrichment Designs for Two Subpopulations Comparing Two Treatments vs. Control
Joshua Betz, Johns Hopkins Bloomberg School of Public Health
Slides
