Document Detail


Likelihood methods for binary responses of present components in a cluster.
MedLine Citation:
PMID:  20825395     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In some biomedical studies involving clustered binary responses (say, disease status), the cluster sizes can vary because some components of the cluster can be absent. When both the presence of a cluster component as well as the binary disease status of a present component are treated as responses of interest, we propose a novel two-stage random effects logistic regression framework. For the ease of interpretation of regression effects, both the marginal probability of presence/absence of a component as well as the conditional probability of disease status of a present component, preserve the approximate logistic regression forms. We present a maximum likelihood method of estimation implementable using standard statistical software. We compare our models and the physical interpretation of regression effects with competing methods from literature. We also present a simulation study to assess the robustness of our procedure to wrong specification of the random effects distribution and to compare finite-sample performances of estimates with existing methods. The methodology is illustrated via analyzing a study of the periodontal health status in a diabetic Gullah population.
Authors:
Xiaoyun Li; Dipankar Bandyopadhyay; Stuart Lipsitz; Debajyoti Sinha
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2010-09-03
Journal Detail:
Title:  Biometrics     Volume:  67     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2011 Jun 
Date Detail:
Created Date:  2011-06-21     Completed Date:  2011-11-09     Revised Date:  2014-09-19    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  629-35     Citation Subset:  IM    
Copyright Information:
© 2010, The International Biometric Society.
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MeSH Terms
Descriptor/Qualifier:
Biometry / methods*
Cluster Analysis
Computer Simulation
Diabetes Complications
Disease*
Humans
Likelihood Functions
Logistic Models
Models, Statistical*
Periodontal Diseases / epidemiology
Probability
Grant Support
ID/Acronym/Agency:
1R01LM009153/LM/NLM NIH HHS; P20 RR017696/RR/NCRR NIH HHS; P20 RR017696-06/RR/NCRR NIH HHS; P20RR017696-06/RR/NCRR NIH HHS; R01 CA069222/CA/NCI NIH HHS; R01 CA069222-11/CA/NCI NIH HHS; R01 CA069222-12/CA/NCI NIH HHS; R01 CA074015/CA/NCI NIH HHS; R01 CA074015-12/CA/NCI NIH HHS; R01 MH054693/MH/NIMH NIH HHS; R01 MH054693-12/MH/NIMH NIH HHS; R01A160373//PHS HHS; R01CA69222/CA/NCI NIH HHS; U10 DA013727/DA/NIDA NIH HHS; U10 DA013727-09/DA/NIDA NIH HHS; U10DA013727-09/DA/NIDA NIH HHS
Comments/Corrections

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