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.

Interesterificação química: alternativa para obtenção de gorduras zero trans

The function of lipids in human nutrition has been intensively debated in the last decade.This context reinforces the concern about controlling the trans fat ingestion, due to its negative implications on health. Interesterification provides an important alternative to modify the consistency of oils and fats without causing formation of trans isomers. This article reports research done towards production of zero trans fats by chemical interesterification, for different industrial purposes. Aspects related to the effect of trans fats on diet, their impact on health and modifications in Brazilian legislation are also covered.