样本大小的确定,用于研究与可变的随访时间
Guogen Shan1, Yahui Zhang1, Xinlin Lu1
1Department of Biostatistics, University of Florida, Gainesville, Florida, USA.
Journal of biopharmaceutical statistics
|February 27, 2025
概括
一个新的统计模型通过准确评估随时间推移的治疗效应,即便随着随访时间表的变化,也提高了临床试验的样本大小计算. 与现有的方法相比,这种方法在非线性疾病进展方面更强大.
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
- 统计建模 统计建模
背景情况:
- 在临床研究中,测试前和测试后设计是评估治疗对照差异的常见方法.
- 现有的样本大小计算方法 (减去,ANCOVA,线性混合模型) 对随访时间的变化和对治疗效应的恒定假设有局限性.
研究的目的:
- 开发一种新的统计模型来比较计划后续时间的治疗对照差异.
- 为了考虑随访时间的变化,并提高样本大小计算的准确性.
- 将新模型的性能与现有方法进行比较.
主要方法:
- 提出了一个新的统计模型,利用spline函数来估计治疗和控制臂轨迹.
- 将新方法与减法,ANCOVA和线性混合模型进行比较.
- 在各种条件下,基于I型错误率,统计能力和样本大小要求的评估性能.
- 将新方法应用于来自阿尔茨海默病试验的数据.
主要成果:
- 这四种方法都有效控制了I型错误率.
- 与减法和线性混合模型相比,新方法和ANCOVA显示出更高的统计能力.
- 新方法在非线性疾病进展的情况下显示出比ANCOVA更强的功率.
- 拟议的模型准确地估计了治疗对照差异,同时管理了随访时间的变化.
结论:
- 开发的统计模型提供了一种有效的方法,用于在临床试验中计算样本大小,随后时间可变.
- 新方法提供了增强的统计能力,特别是对于显示非线性疾病进展的研究.
- 这种方法可以提高纵向研究中治疗效果评估的精度.
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