Abstract:
:We investigate population-averaged (PA) and cluster-specific (CS) associations for clustered binary logistic regression in the context of a longitudinal clinical trial that investigated the association between tooth-specific visual elastase kit results and periodontal disease progression within 26 weeks of follow-up. We address estimation of population-averaged logistic regression models with generalized estimating equations (GEE), and conditional likelihood (CL) and mixed effects (ME) estimation of CS logistic regression models. Of particular interest is the impact of clusters that do not provide information for conditional likelihood methods (non-informative clusters) on inferences based upon the various methodologies. The empirical and analytical results indicate that CL methods yield smaller test statistics than ME methods when non-informative clusters exist, and that CL estimates are less efficient than ME estimates under certain conditions. Moreover, previously reported relationships between population-averaged and cluster-specific parameters appear to hold for the corresponding estimates in the presence of these clusters.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Ten Have TR,Landis JR,Weaver SLdoi
10.1002/sim.4780140407subject
Has Abstractpub_date
1995-02-28 00:00:00pages
413-29issue
4eissn
0277-6715issn
1097-0258journal_volume
14pub_type
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