Bayesian binary regression model: an application to in-hospital death after AMI prediction

Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%.

Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Effects of therapeutic approach on the neonatal evolution of very low birth weight infants with patent ductus arteriosus

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Lateral pterygoid muscle volume and migraine in patients with temporomandibular disorders

Lateral pterygoid muscle (LPM) plays an important role in jaw movement and has been implicated in Temporomandibular disorders (TMDs). Migraine has been described as a common symptom in patients with TMDs and may be related to muscle hyperactivity. This study aimed to compare LPM volume in individuals with and without migraine, using segmentation of the LPM in magnetic resonance (MR) imaging of the TMJ. Twenty patients with migraine and 20 volunteers without migraine underwent a clinical examination of the TMJ, according to the Research Diagnostic Criteria for TMDs. MR imaging was performed and the LPM was segmented using the ITK-SNAP 1.4.1 software, which calculates the volume of each segmented structure in voxels per cubic millimeter. The chi-squared test and the Fisher's exact test were used to relate the TMD variables obtained from the MR images and clinical examinations to the presence of migraine. Logistic binary regression was used to determine the importance of each factor for predicting the presence of a migraine headache. Patients with TMDs and migraine tended to have hypertrophy of the LPM (58.7%). In addition, abnormal mandibular movements (61.2%) and disc displacement (70.0%) were found to be the most common signs in patients with TMDs and migraine. In patients with TMDs and simultaneous migraine, the LPM tends to be hypertrophic. LPM segmentation on MR imaging may be an alternative method to study this muscle in such patients because the hypertrophic LPM is not always palpable.

Anxiety and depression in brazilian undergraduate students: the role of sociodemographic variables, undergraduate course characteristics and social skills

The prevalence of anxiety and depression disorders in undergraduate students is high and several variables can be influential. The aim is to verify the predictive value of social skills, sociodemographic variables, and course characteristics for depression and anxiety. A total of 1282 students of a public university, of both sexes and from different years and courses, participated in this study. Screening instruments for depression and anxiety were applied, as well as an instrument investigating social skills and a questionnaire covering socio demographic indicators and course characteristics. The data were analyzed using univariate analysis followed by multiple binary regression analysis in order to define the relevance of these depression and anxiety measures. The rates of anxiety and depression were 19.4% and 3.8%, respectively. The social skills and living situation were predictive of depression, with the social skills and course area (with higher prevalence for the exact and human sciences) remaining in the final model for anxiety. Such data have implications for psychological prevention and intervention with this population.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Microencapsulation of turmeric oleoresin in binary and ternary blends of gum arabic, maltodextrin and modified starch

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimation of genetic parameters for partial egg production periods by means of random regression models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

The objective of this study was to evaluate the use of probit and logit link functions for the genetic evaluation of early pregnancy using simulated data. The following simulation/analysis structures were constructed: logit/logit, logit/probit, probit/logit, and probit/probit. The percentages of precocious females were 5, 10, 15, 20, 25 and 30% and were adjusted based on a change in the mean of the latent variable. The parametric heritability (h²) was 0.40. Simulation and genetic evaluation were implemented in the R software. Heritability estimates (ĥ²) were compared with h² using the mean squared error. Pearson correlations between predicted and true breeding values and the percentage of coincidence between true and predicted ranking, considering the 10% of bulls with the highest breeding values (TOP10) were calculated. The mean ĥ² values were under- and overestimated for all percentages of precocious females when logit/probit and probit/logit models used. In addition, the mean squared errors of these models were high when compared with those obtained with the probit/probit and logit/logit models. Considering ĥ², probit/probit and logit/logit were also superior to logit/probit and probit/logit, providing values close to the parametric heritability. Logit/probit and probit/logit presented low Pearson correlations, whereas the correlations obtained with probit/probit and logit/logit ranged from moderate to high. With respect to the TOP10 bulls, logit/probit and probit/logit presented much lower percentages than probit/probit and logit/logit. The genetic parameter estimates and predictions of breeding values of the animals obtained with the logit/logit and probit/probit models were similar. In contrast, the results obtained with probit/logit and logit/probit were not satisfactory. There is need to compare the estimation and prediction ability of logit and probit link functions.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimates of genetic parameters for scrotal circumference using random regression models in Nelore cattle

A total of 15,901 scrotal circumference (SC) records from 5300 Nelore bulls, ranging from 229 to 560 days of age, were used with the objective of estimating (co)variance functions for SC, using random regression models. Models included the fixed effects of contemporary group and age of dam at calving as covariable (linear and quadratic effects). To model the population mean trend, a third order Legendre polynomial on animal age was utilized. The direct additive genetic and animal permanent environmental random effects were modeled by Legendre polynomials on animal age, with orders of fit ranging from 1 to 5. Residual variances were modeled considering 1 (homogeneity of variance) or 4 age classes. Results obtained with the random regression models were compared to multi-trait analysis. (Co)variance estimates using multi-trait and random regression models were similar. The model considering a third- and fifth-order Legendre polynomials for additive genetic and animal permanent environmental effects, respectively, was the most adequate to model changes in variance of SC with age. Heritability estimates for SC ranged from 0.24 (229 days of age) to 0.47 (300 days of age), remained almost constant until 500 days of age (0.52), decreasing thereafter (0.44). In general, the genetic correlations between measures of scrotal circumference obtained from 229 to 560 days of age decreased with increasing distance between ages. For genetic evaluation scrotal circumference could be measured between 400 and 500 days of age. (C) 2010 Elsevier B.V. All rights reserved.

Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Random regression models to estimate genetic parameters for test-day milk yield in Brazilian Murrah buffaloes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)