Document Detail


Bayesian isotonic regression and trend analysis.
MedLine Citation:
PMID:  15180665     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In many applications, the mean of a response variable can be assumed to be a nondecreasing function of a continuous predictor, controlling for covariates. In such cases, interest often focuses on estimating the regression function, while also assessing evidence of an association. This article proposes a new framework for Bayesian isotonic regression and order-restricted inference. Approximating the regression function with a high-dimensional piecewise linear model, the nondecreasing constraint is incorporated through a prior distribution for the slopes consisting of a product mixture of point masses (accounting for flat regions) and truncated normal densities. To borrow information across the intervals and smooth the curve, the prior is formulated as a latent autoregressive normal process. This structure facilitates efficient posterior computation, since the full conditional distributions of the parameters have simple conjugate forms. Point and interval estimates of the regression function and posterior probabilities of an association for different regions of the predictor can be estimated from a single MCMC run. Generalizations to categorical outcomes and multiple predictors are described, and the approach is applied to an epidemiology application.
Authors:
Brian Neelon; David B Dunson
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  60     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  2004 Jun 
Date Detail:
Created Date:  2004-06-07     Completed Date:  2005-01-14     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  398-406     Citation Subset:  IM    
Affiliation:
Department of Biostatistics, CB no. 7420, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem*
Biometry*
Dichlorodiphenyl Dichloroethylene / toxicity
Epidemiologic Methods
Female
Humans
Infant, Newborn
Insecticides / toxicity
Linear Models
Markov Chains
Monte Carlo Method
Pregnancy
Premature Birth / chemically induced
Regression Analysis*
Chemical
Reg. No./Substance:
0/Insecticides; 72-55-9/Dichlorodiphenyl Dichloroethylene

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


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