Resources on Adaptive Enrichment Trial Designs
- Instructions for Accessing Our Open-Source 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
- 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
- Rosenblum, M., Fang, X., and Liu, H. (2020) Optimal, Two Stage, Adaptive Enrichment Designs for Randomized Trials Using Sparse Linear Programming. Journal of the Royal Statistical Society, Series B.
- Steingrimsson, J.A., Betz, J., Qian, T., and Rosenblum, M. (2019) Optimized Adaptive Enrichment Designs for Three-Arm Trials: Learning which Subpopulations Benefit from Different Treatments. Biostatistics.
- Fisher, A., Rosenblum, M. & for the Alzheimer’s Disease Neuroimaging Initiative (2018) Stochastic Optimization of Adaptive Enrichment Designs for Two Subpopulations. Journal of Biopharmaceutical Statistics, 28(5), 966-982.
- Rosenblum, M., and Hanley, D.F. (2017) Topical Review: Adaptive Enrichment Designs for Stroke Clinical Trials. Stroke. 48(6). 2021–2025.
Resources on Analysis Methods for Improving Precision and Power by Adjusting for Baseline Variables in Randomized Trials
- Benkeser, D., Diaz, I., Luedtke, A., Segal, J., Scharfstein, D., and Rosenblum, M. (2020) Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, or Time to Event Outcomes. Biometrics. This paper was selected to be a discussion paper.
- Video on Using Covariate Adjustment to Improve Precision in Randomized Clinical Trials (Part of Project Didact)
- ENAR 2020 slides: Machine Learning for Leveraging Prognostic
Baseline Variables to Gain Precision and Reduce Sample Size in Randomized Trials
- Steingrimsson, J. A., Hanley, D. F., and Rosenblum, M. (2017) Improving Precision by Adjusting for Baseline Variables in Randomized Trials with Binary Outcomes, without Regression Model Assumptions. Contemporary Clinical Trials. 54. 18-24.
- Wang, B., Ogburn, E., and Rosenblum, M. (2019) Analysis of Covariance (ANCOVA) in Randomized Trials: More Precision and Valid Confidence Intervals, Without Model Assumptions. Biometrics. 75(4)
- R code for use in trial planning, to estimate precision gain from covariate adjustment based on previous trial or observational study data set.
- R and SAS code implementing covariate adjustment for binary outcome as described in Colantuoni, E. and Rosenblum, M. (2015) Leveraging Prognostic Baseline Variables to Gain Precision in Randomized Trials. Statistics in Medicine. 34(18), 2602-2617. (with Erratum)
- R code implementing covariate adjustment for time-to-event outcome, when estimating restricted mean survival time as described in Dıaz, I., Colantuoni, E., Hanley, D. F., and Rosenblum, M. (2019) Improved Precision in the Analysis of Randomized Trials with Survival Outcomes, without Assuming Proportional Hazards. Lifetime Data Analysis. (with Erratum)
- R and SAS code implementing targeted maximum likelihood estimator for repeated measures outcome as described in Rosenblum, M., McDermott, A., and Colantuoni, E. (2018) Robust Estimation of the Average Treatment Effect in Alzheimer's Disease Clinical Trials.
Also see the following papers with example applications of covariate adjustment to improve precision 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
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