Sample size calculation for multi-arm parallel design with restricted mean survival time

• Published in: Statistical methods in medical research Vol. 33; no. 1; pp. 130 - 147
• Main Authors: Chen, Yaxian, Lam, Kwok Fai, Xu, Jiajun
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-01-2024; Sage Publications Ltd
• Subjects: Chi-square test; Clinical research; Clinical trials; Design; Flexibility; Humans; Medical technology; Oncology; Penetration; Proportional Hazards Models; Research Design; Robustness; Sample Size; Simulation; Size determination; Survival; Survival analysis; Survival Rate; Tests; Multi-arm design; global test; RMST; sample size; multiple testing; dose-finding
• ISSN: 0962-2802; 1477-0334
• DOI: 10.1177/09622802231219852

With the recent advances in oncology treatment, restricted mean survival time (RMST) is increasingly being used to replace the routine approach based on hazard ratios in randomized controlled trials for time-to-event outcomes. While RMST has been widely applied in single-arm and two-arm designs, challenges still exist in comparing RMST in multi-arm trials with three or more groups. In particular, it is unclear in the literature how to compare more than one intervention simultaneously or perform multiple testing based on RMST, and sample size determination is a major obstacle to its penetration to practice. In this paper, we propose a novel method of designing multi-arm clinical trials with right-censored survival endpoint based on RMST that can be applied in both phase II/III settings using a global
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2
test as well as a modeling-based multiple comparison procedure. The framework provides a closed-form sample size formula built upon a multi-arm global test and a sample size determination procedure based on multiple-comparison in the phase II dose-finding study. The proposed method enjoys strong robustness and flexibility as it requires less a priori set-up than conventional work, and obtains a smaller sample size while achieving the target power. In the assessment of sample size, we also incorporate practical considerations, including the presence of non-proportional hazards and staggered patient entry. We evaluate the validity of our method through simulation studies under various scenarios. Finally, we demonstrate the accuracy and stability of our method by implementing it in the design of two real clinical trial examples.