Document Detail


Likelihood methods for binary responses of present components in a cluster.
MedLine Citation:
PMID:  20825395     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Summary 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     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:  -     Revised Date:  -    
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.
Affiliation:
Department of Statistics, Florida State University, Tallahassee, Florida 32306, U.S.A. Division of Biostatistics and Epidemiology, Medical University of South Carolina, Charleston, South Carolina 29425, U.S.A. Division of General Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, U.S.A.
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