Extending multivariate Student's- semiparametric mixed models for longitudinal data with censored responses and heavy tails.

This article extends the semiparametric mixed model for longitudinal censored data with Gaussian errors by considering the Student's -distribution. This model allows us to consider a flexible, functional dependence of an outcome variable over the covariates using nonparametric regression. Moreover, the proposed model takes into account the correlation between observations by using random effects.

Revisiting the use of proton magnetic resonance spectroscopy in distinguishing between primary and secondary malignant tumors of the central nervous system.

Background And Purpose: Conventional magnetic resonance images (MRI) has limitations in distinguishing primary from secondary brain tumors. Proton magnetic resonance spectroscopy (H-MRS) allows evaluation of the concentration of metabolites in a brain lesion and, hence, better characterization of the tumor. Considering that an accurate diagnosis determines the choice of treatment, our purpose was to assess the usefulness of spectroscopy data for differentiating between primary and secondary brain neoplasms.

Poverty and family resilience in Belém-Pará.

Family resilience is a complex, multi-determined behavior caused by the inseparable action of risk and protection factors. The purpose of this paper is to associate aspects of family resilience with multiple dimensions of poverty through a quantitative, descriptive, correlative, exploratory study with a sample of 448 low-income families in thirteen Social Assistance Reference Centers in Belém, Pará. The instruments used in the study were the Family Resilience Profile Questionnaire, the Social and Demographic Inventory, and the Family Poverty Rate.

Scale mixture of skew-normal linear mixed models with within-subject serial dependence.

In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. These studies are commonly analyzed using linear mixed models (LMMs), and in this article we consider an extension of the skew-normal/independent LMM, where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated.

Mixed-effects models for censored data with autoregressive errors.

Mixed-effects models, with modifications to accommodate censored observations (LMEC/NLMEC), are routinely used to analyze measurements, collected irregularly over time, which are often subject to some upper and lower detection limits. This paper presents a likelihood-based approach for fitting LMEC/NLMEC models with autoregressive of order dependence of the error term. An EM-type algorithm is developed for computing the maximum likelihood estimates, obtaining as a byproduct the standard errors of the fixed effects and the likelihood value.

Flexible longitudinal linear mixed models for multiple censored responses data.

In biomedical studies and clinical trials, repeated measures are often subject to some upper and/or lower limits of detection. Hence, the responses are either left or right censored. A complication arises when more than one series of responses is repeatedly collected on each subject at irregular intervals over a period of time and the data exhibit tails heavier than the normal distribution.

Influence assessment in censored mixed-effects models using the multivariate Student's- distribution.

In biomedical studies on HIV RNA dynamics, viral loads generate repeated measures that are often subjected to upper and lower detection limits, and hence these responses are either left- or right-censored. Linear and non-linear mixed-effects censored (LMEC/NLMEC) models are routinely used to analyse these longitudinal data, with normality assumptions for the random effects and residual errors. However, the derived inference may not be robust when these underlying normality assumptions are questionable, especially the presence of outliers and thick-tails.