Growth curve in Santa Ines sheep

The objective of this research was to use non-linear models to describe the growth pattern in Santa Ines sheep and to study the influence of environmental effects on curve parameters with the best-fit model. The models included the Brody, Richards, Von Bertalanffy, Gompertz, and Logistic models. We used 773 field reports on 162 animals ranging in age from 120 to 774 days, including 46 males and 116 females. The statistics used to evaluate the quality of fit included RMS (residual mean square), C% (percentage of convergence), R-2 (adjusted determination coefficient) and MAD (mean absolute deviation). Of the fixed effects studied, the only significant relationship was the effect of sex on parameter A. The Richards model was problematic during the process of convergence. Considering all studied criteria, the Logistic model presented the best fit in describing the growth pattern in Santa Ines sheep. (C) 2011 Elsevier B.V. All rights reserved.

Income inequality and elderly self-rated health in Sao Paulo, Brazil

Purpose: To test the association between income inequality and elderly self-rated health and to propose a pathway to explain the relationship. Methods: We analyzed a sample of 2143 older individuals (60 years of age and over) from 49 distritos of the Municipality of Sao Paulo, Brazil. Bayesian multilevel logistic models were performed with poor self-rated health as the outcome variable. Results: Income inequality (measured by the Gini coefficient) was found to be associated with poor self-rated health after controlling for age, sex, income and education (odds ratio, 1.19; 95% credible interval, 1.01-1.38). When the practice of physical exercise and homicide rate were added to the model, the Gini coefficient lost its statistical significance (P>.05). We fitted a structural equation model in which income inequality affects elderly health by a pathway mediated by violence and practice of physical exercise. Conclusions: The health of older individuals may be highly susceptible to the socioeconomic environment of residence, specifically to the local distribution of income. We propose that this association may be mediated by fear of violence and lack of physical activity. (C) 2012 Elsevier Inc. All rights reserved.

Transmission of Human Herpesvirus Type 8 Infection Within Families in American Indigenous Populations From the Brazilian Amazon

Background. The intrafamilial dynamics of endemic infection with human herpesvirus type 8 (HHV-8) in Amerindian populations is unknown. Methods. Serum samples were obtained from 517 Amerindians and tested for HHV-8 anti-latent nuclear antigen (anti-LANA) and antilytic antibodies by immunofluorescence assays. Logistic regression and mixed logistic models were used to estimate the odds of being HHV-8 seropositive among intrafamilial pairs. Results. HHV-8 seroprevalence by either assay was 75.4% (95% confidence interval [CI]: 71.5%-79.1%), and it was age-dependent (P-trend<.001). Familial dependence in HHV-8 seroprevalence by either assay was found between mother-offspring (odds ratio [OR], 5.44; 95% CI: 1.62-18.28) and siblings aged >= 10 years (OR 4.42, 95% CI: 1.70-11.45) or siblings in close age range (<5 years difference) (OR 3.37, 95% CI: 1.21-9.40), or in families with large (>4) number of siblings (OR, 3.20, 95% CI: 1.33-7.67). In separate analyses by serological assay, there was strong dependence in mother-offspring (OR 8.94, 95% CI: 2.94-27.23) and sibling pairs aged >= 10 years (OR, 11.91, 95% CI: 2.23-63.64) measured by LANA but not lytic antibodies. Conclusions. This pattern of familial dependence suggests that, in this endemic population, HHV-8 transmission mainly occurs from mother to offspring and between close siblings during early childhood, probably via saliva. The mother to offspring dependence was derived chiefly from anti-LANA antibodies.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.