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A Note on Permutation Tests for Variance Components in Multilevel Generalized Linear Mixed Models

Garrett M. Fitzmaurice, Stuart R. Lipsitz and Joseph G. Ibrahim
Biometrics
Vol. 63, No. 3 (Sep., 2007), pp. 942-946
Stable URL: http://www.jstor.org/stable/4541429
Page Count: 5
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A Note on Permutation Tests for Variance Components in Multilevel Generalized Linear Mixed Models
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Abstract

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.

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