Yucel Recai M
Department of Epidemiology and Biostatistics, University at Albany, School of Public Health, One University Place, Room 139, Rensselaer, NY 12144, USA.
Philos Trans A Math Phys Eng Sci. 2008 Jul 13;366(1874):2389-403. doi: 10.1098/rsta.2008.0038.
Methods specifically targeting missing values in a wide spectrum of statistical analyses are now part of serious statistical thinking due to many advances in computational statistics and increased awareness among sophisticated consumers of statistics. Despite many advances in both theory and applied methods for missing data, missing-data methods in multilevel applications lack equal development. In this paper, I consider a popular inferential tool via multiple imputation in multilevel applications with missing values. I specifically consider missing values occurring arbitrarily at any level of observational units. I use Bayesian arguments for drawing multiple imputations from the underlying (posterior) predictive distribution of missing data. Multivariate extensions of well-known mixed-effects models form the basis for simulating the posterior predictive distribution, hence creating the multiple imputations. The discussion of these topics is demonstrated in an application assessing correlates to unmet need for mental health care among children with special health care needs.
由于计算统计学的诸多进展以及精通统计学的用户的意识增强,在广泛的统计分析中专门针对缺失值的方法如今已成为严谨统计思维的一部分。尽管在缺失数据的理论和应用方法方面都有很多进展,但多级应用中的缺失数据方法却缺乏同等的发展。在本文中,我考虑了一种在存在缺失值的多级应用中通过多重填补进行的常用推断工具。我特别考虑了在观测单位的任何层级上任意出现的缺失值。我使用贝叶斯论证从缺失数据的潜在(后验)预测分布中进行多重填补。著名的混合效应模型的多变量扩展构成了模拟后验预测分布的基础,从而创建多重填补。这些主题的讨论在一项评估有特殊医疗需求儿童未满足的心理健康护理需求相关因素的应用中得到了展示。