Association models for periodontal disease progression: a comparison of methods for clustered binary data.

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 Med

journal_title

Statistics in medicine

authors

Ten Have TR,Landis JR,Weaver SL

doi

10.1002/sim.4780140407

subject

Has Abstract

pub_date

1995-02-28 00:00:00

pages

413-29

issue

4

eissn

0277-6715

issn

1097-0258

journal_volume

14

pub_type

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