Fieuws Steffen, Verbeke Geert
Biostatistical Centre, Katholieke Universiteit Leuven, Kapucijnenvoer 35, B-3000 Leuven, Belgium.
Biometrics. 2006 Jun;62(2):424-31. doi: 10.1111/j.1541-0420.2006.00507.x.
A mixed model is a flexible tool for joint modeling purposes, especially when the gathered data are unbalanced. However, computational problems due to the dimension of the joint covariance matrix of the random effects arise as soon as the number of outcomes and/or the number of used random effects per outcome increases. We propose a pairwise approach in which all possible bivariate models are fitted, and where inference follows from pseudo-likelihood arguments. The approach is applicable for linear, generalized linear, and nonlinear mixed models, or for combinations of these. The methodology will be illustrated for linear mixed models in the analysis of 22-dimensional, highly unbalanced, longitudinal profiles of hearing thresholds.
混合模型是用于联合建模目的的灵活工具，尤其是在收集的数据不平衡时。然而，一旦结果的数量和/或每个结果使用的随机效应的数量增加，由于随机效应的联合协方差矩阵的维度而产生的计算问题就会出现。我们提出了一种成对方法，其中拟合所有可能的双变量模型，并根据伪似然论证进行推断。该方法适用于线性、广义线性和非线性混合模型，或这些模型的组合。在分析22维、高度不平衡的听力阈值纵向剖面时，将用线性混合模型来说明该方法。
