PS45: Squeezing More Juice from Patient Data: Enhancing Decision-Making in Adaptive Trials by Using Early Endpoint Data

Early stopping for efficacy or futility, as in group sequential, goldilocks, and promising zone designs, aim to make decisions before the maximum sample size has been reached. These trials can decrease expected sample size without sacrificing power or type I error. Response adaptive randomization, arm dropping, and other randomization manipulation strategies have been researched thoroughly and implemented widely. Adaptive allocation can focus randomization so that more data is collected in on arms of interest. These methods are used broadly and recognized as cutting edge methods that diverge from the classic paradigm of how information is used in a clinical trial. A frontier that has similar goals as the previously mentioned adaptations, but has not been the focus of as much research as the other two is the use of early endpoint data in adaptive decision making. It's a seemingly obvious statistical observation that a well-designed clinical trial should use all data available to it to make decisions. Despite that, it is common to ignore early data about subjects in a clinical trial, often to the extent that only subjects with complete information are included in statistical models. This session will describe how early visit information can be leveraged through multiple imputation in Bayesian adaptive clinical trials. The three talks in this session complement each other, creating a natural flow. The first, by Nick Berry, will present general statistical efficiencies available by incorporating early endpoint data into the interim analyses. The second talk, by Tony Jiang, presents an example of a complex innovative design that used longitudinal patient data to improve the quality of its adaptations. Finally, Telba Irony will present on regulatory perspectives of Bayesian designs, and the origin story of the FDA guidance on the use of Bayesian statistics for medical devices.