Document Detail


Bayesian hierarchical analysis of within-units variances in repeated measures experiments.
MedLine Citation:
PMID:  7997717     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We develop hierarchical Bayesian models for biomedical data that consist of multiple measurements on each individual under each of several conditions. The focus is on investigating differences in within-subject variation between conditions. We present both population-level and individual-level comparisons. We extend the partial likelihood models of Chinchilli et al. with a unique Bayesian hierarchical framework for variance components and associated degrees of freedom. We use the Gibbs sampler to estimate posterior marginal distributions for the parameters of the Bayesian hierarchical models. The application involves a comparison of two cholesterol analysers each applied repeatedly to a sample of subjects. Both the partial likelihood and Bayesian approaches yield similar results, although confidence limits tend to be wider under the Bayesian models.
Authors:
T R Ten Have; V M Chinchilli
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  13     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  1994 Sep 
Date Detail:
Created Date:  1995-01-17     Completed Date:  1995-01-17     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  1841-52     Citation Subset:  IM    
Affiliation:
Center for Biostatistics and Epidemiology, Hershey Medical Center, Pennsylvania State University, Hershey 17033.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Bayes Theorem*
Blood Chemical Analysis / instrumentation
Cholesterol / blood
Confidence Intervals
Data Interpretation, Statistical
Humans
Logistic Models*
Chemical
Reg. No./Substance:
57-88-5/Cholesterol

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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