Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/201917
Type: Artigo de periódico
Title: Augmented Mixed Models For Clustered Proportion Data.
Author: Bandyopadhyay, Dipankar
Galvis, Diana M
Lachos, Victor H
Abstract: Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.
Subject: Bayesian
Kullback-leibler Divergence
Augment
Dispersion Models
Periodontal Disease
Proportion Data
Rights: fechado
Identifier DOI: 10.1177/0962280214561093
Address: http://www.ncbi.nlm.nih.gov/pubmed/25491718
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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