It is a challenge to evaluate experimental treatments where it is suspected that the treatment effect may only be strong for certain subpopulations, such as those having a high initial severity of disease, or those having a particular gene variant. Standard randomized controlled trials can have low power in such situations. They also are not optimized to distinguish which subpopulations benefit from a treatment. With the goal of overcoming these limitations, we consider randomized trial designs in which the criteria for patient enrollment may be changed, in a preplanned manner, based on interim analyses. Since such designs allow data-dependent changes to the population sampled, care must be taken to ensure strong control of the familywise Type I error rate.
Our main contribution is a general method for constructing randomized trial designs that (1) allow changes (based on a prespecified decision rule) to the population enrolled based on interim data, (2) make no parametric model assumptions, and (3) guarantee the asymptotic, familywise Type I error rate is strongly controlled at a specified level. As a demonstration of our method, we prove new, sharp results for a simple, two-stage enrichment design. We then compare this design to a fixed design, focusing on each design's ability to determine overall and subpopulation specific treatment effects.
Statistical Methodology | Statistical Theory
Rosenblum, Michael and van der Laan, Mark J., "Optimizing Randomized Trial Designs to Distinguish which Subpopulations Benefit from Treatment" (June 2010). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 267.