Fitzmaurice Garrett M, Lipsitz Stuart R, Ibrahim Joseph G
Harvard Medical School, Boston, MA, USA.
Biometrics. 2007 Sep;63(3):942-6. doi: 10.1111/j.1541-0420.2007.00775.x. Epub 2007 Apr 2.
In many applications of generalized linear mixed models to multilevel data, it is of interest to test whether a random effects variance component is zero. It is well known that the usual asymptotic chi-square distribution of the likelihood ratio and score statistics under the null does not necessarily hold. In this note we propose a permutation test, based on randomly permuting the indices associated with a given level of the model, that has the correct Type I error rate under the null. Results from a simulation study suggest that it is more powerful than tests based on mixtures of chi-square distributions. The proposed test is illustrated using data on the familial aggregation of sleep disturbance.
在广义线性混合模型在多水平数据的许多应用中，检验随机效应方差分量是否为零是很有意义的。众所周知，在原假设下似然比和得分统计量通常的渐近卡方分布不一定成立。在本笔记中，我们提出了一种置换检验，基于对与模型给定水平相关的指标进行随机置换，该检验在原假设下具有正确的第一类错误率。模拟研究的结果表明，它比基于卡方分布混合的检验更具功效。使用睡眠障碍家族聚集的数据说明了所提出的检验。
