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

Bellamy, SL; Li, Y; Lin, X; Ryan, LM

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

Bellamy, SL Li, Y Lin, X Ryan, LM

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Publication Type:: Journal Article
Citation:: Statistica Sinica, 2005, 15 (4), pp. 1015 - 1032
Issue Date:: 2005-10-01

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Field	Value	Language
dc.contributor.author	Bellamy, SL	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Lin, X	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	2005-10-01	en_US
dc.identifier.citation	Statistica Sinica, 2005, 15 (4), pp. 1015 - 1032	en_US
dc.identifier.issn	1017-0405	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26023
dc.description.abstract	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 O p(1/n) + O p{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.	en_US
dc.relation.ispartof	Statistica Sinica	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Quantifying PQL bias in estimating cluster-level covariate effects in generalized linear mixed models for group-randomized trials	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	15	en_US
utslib.for	010402 Biostatistics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

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 O p(1/n) + O p{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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http://hdl.handle.net/10453/26023