In recent years, clinical trials utilizing a two-stage seamless adaptive trial design have become very popular in drug development. A typical example is a phase 2/3 adaptive trial design, which consists of two stages. As an example, stage 1 is for a phase 2 dose-finding study and stage 2 is for a phase 3 efficacy confirmation study. Depending upon whether or not the target patient population, study objectives, and study endpoints are the same at different stages, Chow (2020) classified two-stage seamless adaptive design into eight categories. In practice, standard statistical methods for group sequential design with one planned interim analysis are often wrongly directly applied for data analysis. In this article, following similar ideas proposed by Chow and Lin (2015) and Chow (2020), a statistical method for the analysis of a two-stage seamless adaptive trial design with different study endpoints and shifted target patient population is discussed under the fundamental assumption that study endpoints have a known relationship. The proposed analysis method should be useful in both clinical trials with protocol amendments and clinical trials with the existence of disease progression utilizing a two-stage seamless adaptive trial design.
Keywords: Multiple-stage seamless adaptive design; control of type I error rate; population shift; protocol amendment; sample size allocation.