在使用定义变量的离散时间隐性变化得分模型中容纳连续时间指标
Sarfaraz Serang1, Shawn D Whiteman2, Annabelle H Reese1
1University of South Carolina.
概括
这项研究引入了一种新的统计模型,可以精确追踪随时间的变化,考虑到流行病阶段和年龄. 它改进了分析青少年发育趋势的现有方法.
科学领域:
- 统计 统计 统计 统计
- 发展心理学 发展心理学
- 流行病学 流行病学
背景情况:
- 纵向模型传统上使用单一的时间度量来评估变化.
- 随着COVID-19的流行,人们需要在考虑年龄的同时,在不同阶段对变化进行建模.
- 现有的方法可能无法准确地捕捉复杂的时间动态.
研究的目的:
- 扩展离散时间隐性变化得分建模框架.
- 通过结合连续时间指标,精确地建模波对波的变化.
- 在纵向分析中同时考虑年龄和大流行阶段.
主要方法:
- 提议扩展到离散时间隐性变化得分建模.
- 包括通过回归初始年龄的连续时间指标.
- 使用定义变量而不是年龄.
- 将模型应用于青少年大麻期望数据.
- 进行了模拟研究,以将方法与现有模型进行比较.
主要成果:
- 与传统方法相比,拟议的模型提供了一种更精确的方法来分析纵向数据.
- 模拟表明了结合连续时间和定义变量的优势.
- 该模型有效地捕捉了受流行病阶段和年龄影响的变化.
结论:
- 扩展的潜变得分模型为分析复杂的发育变化提供了一个强大的框架.
- 这种方法提高了纵向建模的精度,特别是在流行病等动态时期.
- 这些发现对理解青少年发育和物质使用轨迹有意义.
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