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PubMed · 15696505

HGLM versus conditional estimators for the analysis of clustered binary data.

Weechang Kang·Moo-Song Lee·Youngjo Lee
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Abstract

Clustered binary data arise frequently in medical research such as cross-over clinical trials and twin studies. For the analysis of such data either a random-effects model or a conditional likelihood approach can be used. In this paper, we compare numerically the random-effects model estimator and the conditional likelihood estimator and discuss their relative merits for the analysis of binary data.

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Age FactorsClinical Trials as TopicCluster AnalysisCross-Over StudiesData Interpretation, StatisticalDrug TherapyFemaleHumansMaleModels, StatisticalNumerical Analysis, Computer-AssistedSex FactorsSkin DiseasesTwin Studies as TopicComparative StudyJournal ArticleResearch Support, Non-U.S. Gov't

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Weechang Kang, Moo-Song Lee, Youngjo Lee. 2005-03-15. HGLM versus conditional estimators for the analysis of clustered binary data.. https://doi.org/10.1002/sim.1772

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