使用纵向排列和总和测试的多变量纵向试验的功率和样本大小计算.
Dhrubajyoti Ghosh1, Xiaoming Xu1, Sheng Luo1
1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Statistics in medicine
|September 14, 2025
概括
这项研究引入了一种新的统计方法,用于估计用于评估阿尔茨海默氏症和帕金森症等神经退行性疾病治疗方法的临床试验所需的样本大小,使用纵向排列和值测试 (LRST). 这种方法确保试验有足够的动力来检测多种结果的治疗效应.
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
- 神经科学是一个神经科学.
背景情况:
- 神经退行性疾病 (阿尔茨海默病,帕金森病) 呈现复杂的纵向结果.
- 评估治疗疗效需要对多变量数据的先进统计方法.
- 现有的方法可能需要多重度校正,使分析复杂化.
研究的目的:
- 为纵向排列总和测试 (LRST) 开发强大的功率和样本大小估计方法.
- 为设计复杂的神经退行性疾病的高效,强大的临床试验提供框架.
- 为了促进对多个纵向终点的整体治疗效果的评估,而无需进行多重性校正.
主要方法:
- 开发了一个非参数框架,用于纵向排列总和测试 (LRST).
- 集成的理论导数和非对称属性用于功率和样本大小计算.
- 采用适合大样本条件的实际估计技术.
主要成果:
- 数字模拟验证了LRST提出的功率和样本大小估计方法的准确性.
- 该方法成功地应用于阿尔茨海默病 (AD) 和帕金森病 (PD) 的现实世界临床试验数据.
- 证明了开发的框架在临床环境中的实际意义和适用性.
结论:
- 拟议的方法提供了一个可靠的工具,用于对LRST的样本大小和功率估计.
- 该框架增强了对神经退行性疾病的临床试验的设计,具有多变量纵向结果.
- 促进在复杂疾病研究中更有效和更强大的治疗疗效评估.
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