The FORSS Framework for Sample Size and Power Calculations With Win Statistics for Hierarchical Endpoints
层级终点胜出统计量的样本量与功效计算：FORSS 框架AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要主要研究临床试验层级终点的样本量与功效设计，未明确展开与所列计量细分领域对应的识别问题。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
层级终点胜出统计量的样本量与功效计算：FORSS 框架
- 研究问题
- 采用层级终点胜出统计量的临床试验，如何在保留终点依赖结构的同时，用易指定的边际效应计算样本量和功效？
- 方法与思路
- 作者以边际治疗效应和灵活的联合工作分布生成超样本，估计解析公式所需的总体代入量，减少逐个候选样本量反复模拟。
- 材料陈述的贡献
- 摘要称模拟功效与公式预测接近且第一类错误率接近百分之五，并以 HEART-FID 说明终点依赖设定会影响设计结果。
- 阅读关注点
- 核对胜出统计量和并列事件的定义、联合工作分布的设定、超样本近似误差，以及终点依赖错设时样本量与功效的敏感性。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收状态未核验；摘要未给出模拟场景与误差范围，所报接近名义水平的表现不能视为普遍保证。
原文摘要
Win statistics have gained increasing popularity as primary analysis methods for clinical trials with hierarchical endpoints (HEs) as primary endpoints. However, existing sample size and power calculation approaches in trial design still face several limitations and challenges: simulation-based approaches are computationally intensive, while existing formula-based methods often rely on simplifying assumptions such as independence among HEs, or require specification of overall win statistics and tie probability that are difficult to elicit a priori in practice. To address these challenges, we propose the FORSS framework, a FORmula-based Super-Sample approach that allows investigators to specify marginal treatment effects using familiar metrics (e.g., hazard ratios, mean differences, and risk differences) together with a flexible joint working distribution for the HEs. Rather than repeatedly simulating full trials at each candidate sample size, FORSS uses super-samples to estimate the population-level plug-in quantities required by analytical formulas for both power and sample size calculation. We evaluated the performance of the proposed FORSS through extensive simulation studies. The results show that the formula-based FORSS closely matches empirical power across a wide range of scenarios while maintaining Type~I error rates near the nominal 5\% level. An illustration based on the HEART-FID trial further shows that endpoint-dependence specifications can materially affect projected power and required sample size when planning trials with HEs.