Estimation and Confidence Intervals in Randomized Trials That Adapt Enrollment Criteria

• Activity Number: 547
• Type: Invited
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Social Statistics Section
• Author(s): Michael Rosenblum*+
• Companies: Johns Hopkins Bloomberg School of Public Health
• Keywords: adaptive designs ; estimation ; confidence interval ; enrichment ; subpopulation

Abstract

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. We focus on estimation and construction of confidence intervals. Since our designs allow data-dependent changes to the population enrolled, care must be taken to ensure that the resulting bias and mean squared error in treatment effect estimates is bounded, and that confidence intervals have correct coverage probability. We give a general method for obtaining sharp bounds on bias and mean squared error and for constructing confidence intervals with uniformly correct coverage probability.