发表机构
Adela Inc.（阿德拉公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究建立了灵敏度、特异度等与Wald型统计量的关联，提出统一框架用于设计二分类和生存结局的临床验证研究，可直接计算功效和样本量。
AI 中文摘要
预测测试的临床验证研究通常设计为关注灵敏度（Se）和特异度（Sp），而统计功效常基于回归效应尺度（如风险比、风险比）计算。然而，这些量存在统计关联。本文提供从灵敏度、特异度和疾病患病率（π）到预测风险、风险对比以及针对二分类和固定时限生存结局的Wald型方差、功效和样本量公式的闭式关联。通过C-最优和D-最优原则的统计效率分析，展示了患病率和阈值选择如何影响研究效率，支持初步研究中的快速决策并为后续更大规模研究的设计提供依据。模拟显示在大多数现实场景中校准良好；当事件罕见且测试效应同时非常大时，需要连续性和最小事件校正以稳定近似。本文以冠状动脉钙化评分用于预测2型糖尿病患者发生心血管疾病的案例研究说明该框架。这些公式使研究者可直接从（Se,Sp,π）检查功效和所需入组人数，无需为每个设计候选单独运行模拟。
英文摘要
Clinical validation studies of predictive tests are usually designed to focus on sensitivity ($Se$) and specificity ($Sp$), while statistical power is often calculated on regression-effect scales (e.g., risk ratio, hazard ratio). However, these quantities are statistically connected. Here, we provide closed-form links from sensitivity, specificity, and disease prevalence ($π$) to predictive risks, risk contrasts, and Wald-type variance, power, and sample-size formulas for binary and fixed-horizon survival outcomes. Analyses of statistical efficiency via C- and D-optimal principles demonstrate how prevalence and threshold choices affect study efficiency, supporting rapid decisions in preliminary studies and informing the design of subsequent, larger studies. Simulations show good calibration across most realistic scenarios; when events are rare and test effects are simultaneously very large, continuity and minimum-event corrections are needed to stabilize the approximation. We illustrate the framework with a case study describing use of the coronary artery calcium score for predicting incident cardiovascular disease in patients with type 2 diabetes mellitus. The formulas let investigators check power and required enrollment directly from $(Se,Sp,π)$, without running a separate simulation for each design candidate.
