纵向偏斜调查数据的三部分随机效应模型,有"不适用"的答案
Eugenia Buta1, Patricia Simon2, Ralitza Gueorguieva3
1Department of Biostatistics, Yale University, New Haven, CT.
概括
这项研究引入了一种新的三部分统计模型,以准确分析缺少答案和底部效应的调查数据. 该模型改善了复杂的健康调查的公正趋势估计,例如人口烟草和健康评估 (PATH) 研究.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 调查方法 调查方法
背景情况:
- 调查数据通常只包括对参与者的子集的问题.
- 地板效应,即响应聚集在最低的规模值,是常见的.
- 分析这些数据需要考虑选择和响应模式的方法.
研究的目的:
- 提出一种新的三部分统计模型,用于分析调查数据,包括缺失和底层效应.
- 提高对随时间推移趋势的公正和高效估计.
- 解决分析复杂纵向调查的挑战,如PATH研究.
主要方法:
- 一个由两个后勤子模型和一个截断的正常模型组成的三部分模型.
- 加入随机效应来处理重复观察中的相关性.
- 使用SAS PROC NLMIXED进行的最大概率估计.
主要成果:
- 拟议的三部分模型显示,与更简单的模型相比,偏差明显较低.
- 该模型在模拟中实现了回归系数的更好的覆盖概率.
- 适用于PATH年轻人数据的应用说明了它的实际实用性.
结论:
- 这三部分模型提供了一个强大的方法来分析复杂的调查数据,包括缺失和地板效应.
- 它在偏差和效率方面比传统方法提供了更高的性能.
- 这种方法提高了纵向健康研究趋势分析的准确性.
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