在纵向临床试验中处理缺失的数据:来自儿科心理学文献的三个例子
James Peugh1,2, Constance Mara1,2
1Behavioral Medicine Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Journal of pediatric psychology
|November 7, 2024
概括
这项研究澄清了RM-ANCOVA,GEE和LLMM等纵向数据分析模型的假设. 它指导研究人员在缺失数据的最大概率 (ML) 和多重归算 (MI) 之间进行选择,引入了BLIMP程序.
科学领域:
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
- 临床试验 临床试验
背景情况:
- 复杂的统计模型通常是非独立响应变量的默认值,特别是在纵向试验中.
- 最大概率 (ML) 和多重归算 (MI) 是处理缺失数据的既定方法.
- 最近的文献质疑基于研究设计的ML和MI之间的最佳选择.
研究的目的:
- 定义三个纵向数据分析模型的假设:RM-ANCOVA,GEE和LLMM.
- 在缺失数据分析中澄清ML与MI的选择标准.
- 介绍BLIMP程序用于缺失数据的归算,并证明其在统计软件中的使用.
主要方法:
- 对纵向数据的统计模型进行比较分析.
- 解释反复测量ANCOVA (RM-ANCOVA),通用估计方程 (GEE) 和纵向线性混合模型 (LLMM) 的假设.
- 使用BLIMP在SPSS,Stata和R.中展示最大概率 (ML) 和多重推算 (MI) 技术.
主要成果:
- 对于RM-ANCOVA,GEE和LLMM的基本假设有明确的定义.
- 根据数据特征和研究问题,关于在ML和MI之间进行选择的指导.
- 在不同软件包中使用BLIMP处理缺少的纵向数据的实例.
结论:
- 研究人员可以自信地选择合适的纵向数据分析模型和缺失的数据处理技术.
- 该BLIMP程序提供了一个用户友好的和验证的工具,用于多次归算.
- 使用这些方法和工具,可以有效地管理纵向试验中缺少的数据.
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