2026/07/01 by Qinghua Lian, Kwang Woo Ahn, Soyoung Kim +5
Mathematics · Decision Sciences · #Statistical Methods in Clinical Trials #Optimal Experimental Design Methods #Statistical Methods and Inference
paper · doi:10.1093/biomtc/ujag135
Composite endpoints, which combine multiple events of interest, are commonly used in medical research. Compared to evaluating a single outcome, a composite endpoint analysis captures the full clinical impact of treatment and leverages a greater number of events, potentially reducing the required sample size for the study. While composite endpoints have gained much attention in clinical trials, studying them in group sequential designs remains challenging due to the correlated nature of event data collected from the same individual, as the sequence of test statistics may not possess an independent increments structure. In this paper, we propose both one-sample and two-sample group sequential designs grounded in the mean frequency function, defined as the cumulative count of all recurrent and terminal events over time. Recognizing the lack of independent increments, our proposed method leverages the asymptotic covariance structure of the test statistics to construct group sequential boundaries that control the Type I error rate. Extensive simulation studies show the proposed design controls Type I error well and achieves the desired power. We illustrate the utility of our method through a reanalysis of BMT CTN 1703, a phase III randomized controlled trial that evaluated an experimental therapy for the prevention of adverse outcomes after allogeneic stem cell transplant.