在部分解盲样本大小重新估计时保存I型错误
Ann Marie K Weideman1,2, Kevin J Anstrom1,2, Gary G Koch1
1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Statistics in medicine
|March 14, 2025
概括
本研究引入了一种在临床试验中进行样本大小重新估计 (SSR) 的新型部分不盲目的方法. 这种方法保持了I型错误率,同时允许基于二进制和连续结果的中间数据进行调整.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 统计学方法论 统计学方法论
背景情况:
- 样本大小重新估计 (SSR) 对于适应性临床试验至关重要,可以根据累计数据进行调整.
- 目前的SSR方法采用盲定或非盲定方法,旨在保持I型错误率.
- 需要SSR方法来平衡运营可行性和统计严谨性.
研究的目的:
- 提出和评估在临床试验中重新估计样本大小 (SSR) 的部分不盲目的方法.
- 评估该方法对二进制和连续端点的I型错误率的影响.
- 在各种变异场景下探索和澄清SSR的数学表达式,包括双变异.
主要方法:
- 开发了一种部分解盲的SSR方法,使用没有效果大小的临时数据来保持操作盲目.
- 进行了概念验证和模拟研究,以验证该方法的性能.
- 研究了SSR对二进制和连续数据的同质性,异质性和双变异场景的数学表达式.
主要成果:
- 拟议的部分解盲SSR方法有效地保持了I型错误率.
- 证明了该方法对二进制和连续终点的适用性.
- 衍生和澄清SSR的双方方差数学表达式,表明它们提供了同质性和异质性之间的妥协,有界样本大小估计,以及适应性设计的适合性.
结论:
- 部分解盲的SSR提供了适应性试验设计的可行策略,保持统计完整性.
- 开发的方法在样本大小调整方面提供了灵活性,而不会影响I型错误率.
- 这些发现扩大了SSR方法的实用性,特别是双变异方法,用于更广泛的临床试验设计.
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