Augmented mixed models for clustered proportion data.
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.
['Bandyopadhyay|Dipankar|D|', 'Galvis|Diana M|DM|', 'Lachos|Victor H|VH|']
D001499:Bayes Theorem D056808:Biostatistics D016000:Cluster Analysis D003198:Computer Simulation D003627:Data Interpretation, Statistical D016208:Databases, Factual D006801:Humans D008390:Markov Chains D015233:Models, Statistical D009010:Monte Carlo Method D010510:Periodontal Diseases D016016:Proportional Hazards Models D012044:Regression Analysis D012984:Software
augment proportion data Bayesian Kullback-Leibler divergence dispersion models periodontal disease