Quantifying PQL bias in estimating cluster-level covariate effects in genearlized linear mixed models for group-randomized trials

Statistica Sinica
Publication Type:
Journal Article
Statistica Sinica, 2005, 15 (4), pp. 1015 - 1032
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We derive the asymptotic bias and variance of the penalized quasilikelihood (PQL) estimator of the cluster-level covariate effect in generalized linear mixed models for group-randomized trials where the number of clusters n is small and the cluster size m is large. We show that the asymptotic bias is of order O-p(1/m) and the asymptotic variance is of order Op(1/n) + Op{1/(nm)}. The practical implication of our results is that the PQL method works well in settings involving small numbers of large clusters which are typical in grouped randomized trials. We illustrate the results using simulation studies.
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