Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

Times assíncronos inicializadores para o planejamento da expansão da transmissão de energia elétrica baseados no modelo híbrido linear

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelos de programação linear inteira mista para resolver problemas de otimização de sistemas de distribuição de energia elétrica radiais

Pós-graduação em Engenharia Elétrica - FEIS

Comportamento dinâmico de suspensão eletromecânica: análise da resposta devido ao amortecimento e à rigidez não linear

Pós-graduação em Engenharia Elétrica - FEB

Individual and contextual factors related to dental caries in underprivileged Brazilian adolescents

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

Quality of life and socio-dental impact among underprivileged brazilian adolescents

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

Individual and contextual factors related to dental caries in underprivileged Brazilian adolescents

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

Genetic analysis of the temperament of Nellore cattle using linear and threshold models

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

On Wald Residuals in Generalized Linear Models

A rigorous asymptotic theory for Wald residuals in generalized linear models is not yet available. The authors provide matrix formulae of order O(n(-1)), where n is the sample size, for the first two moments of these residuals. The formulae can be applied to many regression models widely used in practice. The authors suggest adjusted Wald residuals to these models with approximately zero mean and unit variance. The expressions were used to analyze a real dataset. Some simulation results indicate that the adjusted Wald residuals are better approximated by the standard normal distribution than the Wald residuals.

Influence diagnostics in Gaussian spatial linear models

Spatial linear models have been applied in numerous fields such as agriculture, geoscience and environmental sciences, among many others. Spatial dependence structure modelling, using a geostatistical approach, is an indispensable tool to estimate the parameters that define this structure. However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. The purpose of this paper is to use diagnostic techniques to assess the sensitivity of the maximum-likelihood estimators, covariance functions and linear predictor to small perturbations in the data and/or the spatial linear model assumptions. The methodology is illustrated with two real data sets. The results allowed us to conclude that the presence of atypical values in the sample data have a strong influence on thematic maps, changing the spatial dependence structure.

Influence of children's oral health-related quality of life on school performance and school absenteeism

Objective: This study assessed the relation of child oral health-related quality of life with school performance and school absenteeism. Methods: We followed a cross-sectional design with a multistage random sample of 312 12-year-old schoolchildren living in Brazil. The participants completed the child perceptions questionnaire (CPQ1114) that provides information about psychological factors, while their parents or guardians answered questions on their socioeconomic status measured by parents' education level and household income. A dental examination of each child provided information on the prevalence of caries and dental trauma. Data on school performance, which included the results of baseline Brazilian language (Portuguese) tests, and school absenteeism (school days missed) were obtained from the school register. Multilevel linear regression was used to investigate the association among psychological and socioeconomic status and children's school performance. Results: In the multiple model, after adjusting for individual covariates, being a girl was associated with higher school performance (P < 0.05), whereas low household income (P < 0.05), higher mean of CPQ1114 (P < 0.05), and higher school days missed (P < 0.001) were identified as individual determinants of lower school performance. When the school-level covariates were included in the model, the association between subjects' level characteristics and school performance still persisted. Conclusion: Children's school performance and absence were influenced by psychological and socioeconomic conditions.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models

Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are illustrated with two examples and their validity for cases with practical sample sizes is demonstrated via a simulation study.

Model Checking for ROC Regression Analysis

The Receiver Operating Characteristic (ROC) curve is a prominent tool for characterizing the accuracy of continuous diagnostic test. To account for factors that might invluence the test accuracy, various ROC regression methods have been proposed. However, as in any regression analysis, when the assumed models do not fit the data well, these methods may render invalid and misleading results. To date practical model checking techniques suitable for validating existing ROC regression models are not yet available. In this paper, we develop cumulative residual based procedures to graphically and numerically assess the goodness-of-fit for some commonly used ROC regression models, and show how specific components of these models can be examined within this framework. We derive asymptotic null distributions for the residual process and discuss resampling procedures to approximate these distributions in practice. We illustrate our methods with a dataset from the Cystic Fibrosis registry.

Clinical and economic outcomes of serious postoperative infections following resection of common respiratory and gastrointestinal solid tumors

Unlike infections occurring during periods of chemotherapy-induced neutropenia, postoperative infections in patients with solid malignancy remain largely understudied. The purpose of this population-based study was to evaluate the clinical and economic burden, as well as the relationship of hospital surgical volume and outcomes associated with serious postoperative infection (SPI) – i.e., bacteremia/sepsis, pneumonia, and wound infection – following resection of common solid tumors.^ From the Texas Discharge Data Research File, we identified all Texas residents who underwent resection of cancer of the lung, esophagus, stomach, pancreas, colon, or rectum between 2002 and 2006. From their billing records, we identified ICD-9 codes indicating SPI and also subsequent SPI-related readmissions occurring within 30 days of surgery. Random-effects logistic regression was used to calculate the impact of SPI on mortality, as well as the association between surgical volume and SPI, adjusting for case-mix, hospital characteristics, and clustering of multiple surgical admissions within the same patient and patients within the same hospital. Excess bed days and costs were calculated by subtracting values for patients without infections from those with infections computed using multilevel mixed-effects generalized linear model by fitting a gamma distribution to the data using log link.^ Serious postoperative infection occurred following 9.4% of the 37,582 eligible tumor resections and was independently associated with an 11-fold increase in the odds of in-hospital mortality (95% Confidence Interval [95% CI], 6.7-18.5, P < 0.001). Patients with SPI required 6.3 additional hospital days (95% CI, 6.1 - 6.5) at an incremental cost of $16,396 (95% CI, $15,927–$16,875). There was a significant trend toward lower overall rates of SPI with higher surgical volume (P=0.037). ^ Due to the substantial morbidity, mortality, and excess costs associated with SPI following solid tumor resections and given that, under current reimbursement practices, most of this heavy burden is borne by acute care providers, it is imperative for hospitals to identify more effective prophylactic measures, so that these potentially preventable infections and their associated expenditures can be averted. Additional volume-outcomes research is also needed to identify infection prevention processes that can be transferred from higher- to lower-volume providers.^