基于设计效用的临床试验样本量校准
Calibration of clinical trial sample size based on design utility
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中文总结 AI 辅助
本研究针对临床试验样本量校准的现有问题，提出设计效用指数作为正式依据，平衡检验效能提升与最小可检测效应降低，可防范过度检验效能，符合监管预期。
中文摘要 AI 辅助
临床试验设计需同时结合统计与临床考量，预先设定可能改变临床实践的目标治疗效应。由于更大规模的试验往往对应更高的检验效能（power）和适度的最小可检测获益，试验样本量通常参照相关先例进行校准，以避免过度检验效能（overpowering）。尽管试验发起方与监管机构已习惯这一做法，但仍存在简化空间，以提升设计流程在该环节的稳健性、透明度及跨试验一致性。为此，本文提出一种设计效用指数（design utility index），作为样本量校准的正式依据，其在更高样本量带来的检验效能提升，与对应最小可检测治疗效应幅度的降低之间进行平衡，无需额外统计假设或定制软件。将效用校准应用于广泛的设计场景，显示其与监管机构对最低检验效能的预期一致，尤其在确证性研究中，且能有效防范过度检验效能及过于激进的期中分析。
英文摘要
Clinical trial design relies on both statistical and clinical considerations for pre-specification of potentially practice-changing target treatment effects. As larger trials tend to be associated with high power and modest minimal detectable benefit, trial sample size is typically calibrated with reference to relevant precedents to prevent overpowering. Albeit trial sponsors and regulators are accustomed to this practice, there is scope for simplification to enhance the robustness, transparency and cross-trial consistency of this aspect of the design process. To this end, a design utility index is proposed here as a formal basis for sample size calibration, balancing the increase in power at higher sample sizes against the corresponding reduction in the magnitude of minimum detectable treatment effects, without requiring additional statistical assumptions or bespoke software. Application of utility calibration to a broad range of designs demonstrates consistency with regulatory expectations for minimum power, particularly in confirmatory settings, and effective protection against overpowering and against overly aggressive interim analyses.
