All Times EDT
Keywords: sub-population selection, adaptive enrichment, dual criterion
Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. A well-known challenge in early phase subgroup identification is the small sample size. A standard practice is to enroll a broad range of patients and run post-hoc subset analysis to determine those who may particularly benefit. This unnecessarily exposes many patients to hazardous side effects, and may vastly decrease the efficiency of the trial. The ability to select a correct target patient subpopulation in the early stages increases the probability of success of a targeted therapy. Finally, it is also important to have an evidence-based and transparent quantitative framework in early phase trial for better decision-making. In this work, we propose an adaptive and dual criterion design for sub-population selection in early phase trials. In contrast to the standard design, the proposed design considers both statistical significance and clinical relevance in the GO/NO-GO decision-making. In addition, the proposed design allows selection of appropriate sub-population for the confirmatory phase. If properly implemented, such design identifies the correct population to be benefited by the treatment and uses the available resources appropriately. The proposed design is discussed for single-arm phase II trials for binary and time-to-event endpoints, including decision criteria, sample size calculations, decisions under various data scenarios, and operating characteristics. The designs facilitate GO/NO-GO decisions due to their complementary statistical–clinical criterion and intrinsic population selection algorithm.