在部分集群随机对照试验中处理缺失的数据
Manshu Yang1, Darrell J Gaskin2
1Department of Psychology, University of Rhode Island.
Psychological methods
|November 6, 2023
概括
在部分集群试验中处理缺失的数据对于准确的心理学研究至关重要. 使用联合建模 (MI-JM-AS) 的特定臂式多重归算对于固定效应是最好的,而 MI-SMC-AS 则是随机效应的首选.
科学领域:
- 心理学研究 心理学研究
- 生物统计学 生物统计学
- 临床试验 临床试验
背景情况:
- 部分集群设计在心理随机对照试验中很常见.
- 缺少的数据在这些复杂的试验设计中带来了重大挑战.
- 处理缺失的数据对于有效的研究结论至关重要.
研究的目的:
- 在部分集群研究中比较五种处理辅助变量依赖失踪随机数据的方法.
- 为不同的数据结构确定最有效的归算或估计策略.
- 为处理复杂试验设计中缺少数据的研究人员提供指导.
主要方法:
- 进行了一项模拟研究,以比较五种统计方法.
- 方法包括各种多重归算 (MI) 和顺序完全贝叶斯式 (SFB) 方法.
- 具体的方法进行了比较:MI-JM-SIM,MI-JM-AS,MI-SMC-AS,SFB-NON,SFB-WEAK. 这两种方法是不同的.
主要成果:
- 使用联合建模 (MI-JM-AS) 方法的特定臂的多重归算在缺失变量只涉及固定效应时显示出优异的性能.
- 当不完整的变量包括随机效应时,使用实质模型兼容序列建模 (MI-SMC-AS) 方法进行特定臂的多重归算是首选的.
- 性能根据不完整的变量中存在固定的和随机效应的存在而有所不同.
结论:
- 在部分集群试验中处理缺失数据的方法的选择取决于不完整变量的性质 (固定的与随机效应).
- 在特定条件下建议使用MI-JM-AS和MI-SMC-AS,以提高准确性.
- 实证数据示例说明了这些方法的实际应用.
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