Augmented mixed models for clustered proportion data

Dipankar Bandyopadhyay, Diana M. Galvis, Victor H. Lachos

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)880-897
Number of pages18
JournalStatistical methods in medical research
Volume26
Issue number2
DOIs
StatePublished - Apr 1 2017

Bibliographical note

Publisher Copyright:
© The Author(s) 2014.

Keywords

  • Bayesian
  • Kullback-Leibler divergence
  • augment
  • dispersion models
  • periodontal disease
  • proportion data

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