贝叶斯的两步多重推算方法基于混合模型,用于缺少EMA数据的混合模型.
Yiheng Wei1, Juned Siddique2, Bonnie Spring3
1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA.
Statistics in medicine
|November 19, 2025
概括
多重归算通过解决缺失的数据来增强生态瞬间评估 (EMA) 的统计分析. 选择正确的混合模型,如MELS,对于EMA研究的准确结果至关重要.
科学领域:
- 心理学科学 心理学科学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 生态瞬间评估 (EMA) 提供了丰富的纵向数据,但往往存在重大缺失.
- 缺少的数据可能会损害EMA研究中的统计分析的可靠性.
- 多重归算是处理EMA中缺少数据的一个关键技术.
研究的目的:
- 为EMA数据引入一种新的两步贝叶斯多重归算框架.
- 在这个框架内比较三个混合模型的性能:随机交叉线性混合,混合效应位置尺度 (MELS) 和共享参数MELS.
- 评估处理EMA数据中同时缺失变量的有效性.
主要方法:
- 开发了一种使用混合模型的两步贝叶斯多重归算框架.
- 我们比较了三种归算模型:随机交叉线性混合,MELS和共享参数MELS.
- 进行了模拟研究,以评估EMA数据中同时缺失变量的归算有效性.
主要成果:
- 对EMA数据而言,多次归算显著优于单次归算.
- 归算模型的选择对分析结果产生了重大影响.
- 在特定场景中,MELS模型,特别是在考虑主体内部变异和将缺失与响应联系时,显示出更好的性能.
- 将框架应用于"做出更好的选择1 (MBC1) "研究,证明模型之间的归算结果的差异.
结论:
- 拟议的贝叶斯多重归算框架有效地解决了EMA缺少的数据.
- 选择合适的混合模型,特别是那些捕捉主体内部变异 (MELS) 的模型,对于稳健的EMA分析至关重要.
- 这些发现强调了考虑缺失机制及其与响应变量关系的重要性.
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