Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms
Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms
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DOI：
10.1111/rssa.12022
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发表时间：
2014-02-01
影响因子：
2
通讯作者：
Browne, William J.
中科院分区：
文献类型：
--
作者：
Goldstein, Harvey;Carpenter, James R.;Browne, William J.
本文扩展了现有的多层次多变量数据模型的混合响应类型，以处理相当一般的类型和模式的缺失数据值在广泛的多层次广义线性模型。它提出了一种有效的贝叶斯建模方法，允许协变量中的缺失值，包括存在协变量的相互作用或其他函数（如多项式）的模型。该程序也可用于产生多重插补的完整数据集。模拟研究以及纵向数据集的分析。本文还展示了如何现有的多进程模型处理endothelin可以扩展所提出的框架。
The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models. It proposes an efficient Bayesian modelling approach that allows missing values in covariates, including models where there are interactions or other functions of covariates such as polynomials. The procedure can also be used to produce multiply imputed complete data sets. A simulation study is presented as well as the analysis of a longitudinal data set. The paper also shows how existing multiprocess models for handling endogeneity can be extended by the framework proposed.