Statistical Inference for a Two-Stage Adaptive Seamless Design Using Different Binary Endpoints.

ABSTRACT: Adaptive seamless design, which integrates phases II and III into a single trial comprising two stages, is garnering increasing interest in the efficient drug development. The first stage involves selecting promising treatment group(s), followed by comparing the efficacy between the selected and control groups in the second stage. This study focused on a two-stage adaptive seamless design where treatment selection is based on a short-term binary endpoint, while the comparison is based on a long-term binary endpoint. Recently, exact and mid- p$$ p $$ tests were proposed in this setting. However, treatment effects at the second stage were estimated using the conventional maximum likelihood estimator (MLE), leading to upward bias owing to treatment selection. We propose the conditional mean-a

SUBMITTER: Ishii R 

PROVIDER: S-EPMC11884423 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature