在随机交叉试验的分析中,对灵活的线性混合效应模型进行评估
Moses Mwangi1,2, Geert Verbeke1,3, Edmund Njeru Njagi4
1I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium.
Pharmaceutical statistics
|December 26, 2023
概括
与现有方法相比,新的零碎线性混合效应 (PLME) 模型为分析交叉试验提供了更高的精度. 这种先进的模型更好地估计了随机效应,提高了临床研究中治疗疗效的准确性.
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:
- 交叉设计在随机临床试验中对于估计治疗疗效至关重要,通过对象内部比较提供更高的精度.
- 对交叉试验的分析,特别是关于重复测量的分析,最近有了统计学上的发展.
- 为分析交叉试验,已经提出了一种零碎线性混合效应 (PLME) 模型.
研究的目的:
- 为了比较PLME模型与两个既有模型的性能:Grizzle的混合效应 (GME) 和Jones & Kenward的混合效应 (JKME) 模型.
- 通过模拟的2x2交叉设计,在单变量建模框架中评估这些模型.
- 评估模型收性和随机效应的方差-协方差矩阵估计.
主要方法:
- 进行了一项模拟研究,通过从透气血压 (DBP) 的经验数据中推导真实参数来反映现实生活场景.
- 模拟模拟了一个2x2交叉设计,使用DBP数据估计的固定效应,随机效应和剩余误差的参数.
- 性能评估是基于随机效应的变异-共变矩阵 (G) 和模型趋同的估计.
主要成果:
- 单变量PLME模型在估计随机效应的方差-共变量矩阵 (G) 方面表现优于GME和JKME模型.
- PLME模型实现了令人满意的模型融合,特别是在完全指定随机拦截和斜率时.
- 对于模拟更多的随机效应来说,PLME模型是有利的,这可能解释了更多的数据变化,并提高了精度.
结论:
- 与GME和JKME模型相比,PLME模型在交叉试验分析中可以更好地估计随机效应和差异共变矩阵.
- 当纳入多个随机效应时,PLME模型特别有利,从而提高了效应大小估计的精度.
- 这些发现支持PLME模型的实用性,用于更准确,更稳健地分析交叉临床试验数据.
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