Problem
Research questions and friction points this paper is trying to address.
clinical trial design
sample size calibration
design utility
Calibration of clinical trial sample size based on design utility
🤖 AI Summary
论文提出了一种设计效用指数，用于临床试验样本量的校准，平衡了高样本量带来的功率增加与最小可检测效应值减小之间的关系。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
design utility index
sample size calibration
clinical trial design
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