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一种启动式方法,用于评估隐性类增长模型识别的群体数量的不确定性
American journal of epidemiology
|June 29, 2023
概括
这项研究引入了一个引导方法,用于验证轨迹建模中的组数,提高医学研究的统计可靠性. 它量化了不确定性,提高了纵向数据分析的准确性.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
背景情况:
- 纵向有限混合模型,包括基于组的轨迹建模,在医学研究中越来越多地使用.
- 这些数据驱动的方法面临批评,因为模型选择所涉及的固有统计决策.
- 验证已识别的群体数量和量化相关的不确定性是关键的挑战.
研究的目的:
- 提出和评估一种基于引导的方法,用于验证纵向有限混合模型中的组数.
- 量化与已识别的组结构相关的不确定性.
- 评估在识别这种不确定性时,共同模型充分性标准的表现.
主要方法:
- 采用一个引导重新采样技术,采样观察结果并从原始数据集中替换.
- 拟议的方法通过评估整个引导样本的解决方案一致性来验证组号.
- 一项模拟研究检查了引导式估计变异性和群体数量的复制性变异性之间的关系.
主要成果:
- 引导式方法有效验证了组数,并量化了纵向有限混合模型中的不确定性.
- 模拟结果表明,启动过程中估计的可变性准确地反映了复制过程中的可变性.
- 充分性标准,如平均后方概率,正确分类的几率和相对,被评估为它们检测不确定性的能力.
结论:
- 拟议的引导方法提供了一个统计学上合理的方法来验证群体结构,并评估基于群体的轨迹建模中的不确定性.
- 这种技术提高了从纵向医学数据中得出的发现的可靠性.
- 应用到现实世界的数据确定了糖尿病老年人的独特的纵向药物治疗模式,证明了实际效用.
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