The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing ...

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing ...

In this paper, we present different ofrailtyo models to analyze longitudinal data in the presence of covariates. These models incorporate the extra-Poisson variability and the possible correlation among the repeated counting ...

OBJECTIVE: The objective of this study was to identify trajectories of depressive symptoms (DSs) during the first half of drug-sensitive pulmonary tuberculosis (PTB) treatment and examine their association with loss to ...

This paper presents the results obtained from geophysical data processing, which were acquired through vertical electrical soundings developed on occurrence area of the Guarani Aquifer System (GAS), in São Paulo state, ...

Linear mixed effects models are frequently used to analyse longitudinal data, due to their flexibility in modelling the covariance structure between and within observations. Further, it is easy to deal with unbalanced data, ...

We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression ...