| Full text | |
| Author(s): |
Bandyopadhyay, Dipankar
;
Galvis, Diana M.
;
Lachos, Victor H.
Total Authors: 3
|
| Document type: | Journal article |
| Source: | STATISTICAL METHODS IN MEDICAL RESEARCH; v. 26, n. 2, p. 880-897, APR 2017. |
| Web of Science Citations: | 2 |
| 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. (AU) | |
| FAPESP's process: | 14/02938-9 - Estimation and diagnostics for censored mixed effects models using scale mixtures of skew-normal distributions |
| Grantee: | Víctor Hugo Lachos Dávila |
| Support Opportunities: | Regular Research Grants |