[工作相关性结构矩阵的错误指定对2×2交叉设计中的样本大小估计的影响:模拟研究]
Peiyu Zhang1, Ziheng Xie1, Yan Zhuang1
1Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
|November 28, 2025
概括
在通用估计方程 (GEE) 中错误地指定工作相关性结构可能会对2x2交叉试验的样本大小估计产生重大影响. 然而,在特定条件下,这种影响可能是最小的.
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:
- 样本大小估计对于临床试验的有效性和效率至关重要.
- 一般估计方程 (GEE) 常用于分析纵向或集群数据.
- 工作相关性结构矩阵是GEE模型的关键组成部分.
研究的目的:
- 评估错误指定工作相关性结构矩阵对使用GEE的2x2交叉设计中的样本大小计算的影响.
- 识别错误规范导致估计样本大小显著偏差的条件.
主要方法:
- 蒙特卡洛模拟被用来评估在各种条件下的样本大小估计.
- 控制的关键因素包括总样本大小 (n),序列比例 (θ),相关系数 (ρ) 和安慰剂效应 (OR).
- 偏差和平均平方误差 (MSE) 用于量化估计和理论样本大小之间的差异.
主要成果:
- 正确指定独立工作相关性结构产生了比不正确规范更好的样本大小估计.
- 当同等关联结构被正确指定,并且相关系数接近于零时 (小的n, θ和OR),错误指定有时会导致更少的偏差.
结论:
- 错误地指定工作相关性结构矩阵通常会导致估计样本大小的显著偏差.
- 在某些特定场景下,错误规范对样本大小估计的影响可以被忽略不计.
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