| Testing logistic regression coefficients with clustered data and few positive outcomes. | |
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MedLine Citation:
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PMID: 17705348 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Applications frequently involve logistic regression analysis with clustered data where there are few positive outcomes in some of the independent variable categories. For example, an application is given here that analyzes the association of asthma with various demographic variables and risk factors using data from the third National Health and Nutrition Examination Survey, a weighted multi stage cluster sample. Although there are 742 asthma cases in all (out of 18,395 individuals), for one of the categories of one of the independent variables there are only 25 asthma cases (out of 695 individuals). Generalized Wald and score hypothesis tests, which use appropriate cluster-level variance estimators, and a bootstrap hypothesis test have been proposed for testing logistic regression coefficients with cluster samples. When there are few positive outcomes, simulations presented in this paper show that these tests can sometimes have either inflated or very conservative levels. A simulation-based method is proposed for testing logistic regression coefficients with cluster samples when there are few positive outcomes. This testing methodology is shown to compare favorably with the generalized Wald and score tests and the bootstrap hypothesis test in terms of maintaining nominal levels. The proposed method is also useful when testing goodness-of-fit of logistic regression models using deciles-of-risk tables. |
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Authors:
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Sally Hunsberger; Barry I Graubard; Edward L Korn |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Statistics in medicine Volume: 27 ISSN: 0277-6715 ISO Abbreviation: Stat Med Publication Date: 2008 Apr |
Date Detail:
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Created Date: 2008-03-05 Completed Date: 2008-06-17 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8215016 Medline TA: Stat Med Country: England |
Other Details:
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Languages: eng Pagination: 1305-24 Citation Subset: IM |
Affiliation:
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Biometric Research Branch, National Cancer Institute, Bethesda, MD 20892, U.S.A. sallyh@ctep.nci.nih.gov |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Adult Algorithms Asthma / epidemiology Cluster Analysis* Computer Simulation Female Humans Logistic Models* Male Middle Aged Nutrition Surveys Risk Factors United States / epidemiology |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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