Análise de resíduos em modelos de tempo de falha acelerado com efeito aleatório

We present residual analysis techniques to assess the fit of correlated survival data by Accelerated Failure Time Models (AFTM) with random effects. We propose an imputation procedure for censored observations and consider three types of residuals to evaluate different model characteristics. We illustrate the proposal with the analysis of AFTM with random effects to a real data set involving times between failures of oil well equipment

The safe motherhood referral system to reduce cesarean sections and perinatal mortality - a cross-sectional study [1995-2006]

Background: In 2000, the eight Millennium Development Goals (MDGs) set targets for reducing child mortality and improving maternal health by 2015.Objective: To evaluate the results of a new education and referral system for antenatal/intrapartum care as a strategy to reduce the rates of Cesarean sections (C-sections) and maternal/perinatal mortality.Methods: Design: Cross-sectional study. Setting: Department of Gynecology and Obstetrics, Botucatu Medical School, São Paulo State University/UNESP, Brazil. Population: 27,387 delivering women and 27,827 offspring. Data collection: maternal and perinatal data between 1995 and 2006 at the major level III and level II hospitals in Botucatu, Brazil following initiation of a safe motherhood education and referral system. Main outcome measures: Yearly rates of C-sections, maternal (/100,000 LB) and perinatal (/1000 births) mortality rates at both hospitals. Data analysis: Simple linear regression models were adjusted to estimate the referral system's annual effects on the total number of deliveries, C-section and perinatal mortality ratios in the two hospitals. The linear regression were assessed by residual analysis (Shapiro-Wilk test) and the influence of possible conflicting observations was evaluated by a diagnostic test (Leverage), with p < 0.05.Results: Over the time period evaluated, the overall C-section rate was 37.3%, there were 30 maternal deaths (maternal mortality ratio = 109.5/100,000 LB) and 660 perinatal deaths (perinatal mortality rate = 23.7/1000 births). The C-section rate decreased from 46.5% to 23.4% at the level II hospital while remaining unchanged at the level III hospital. The perinatal mortality rate decreased from 9.71 to 1.66/1000 births and from 60.8 to 39.6/1000 births at the level II and level III hospital, respectively. Maternal mortality ratios were 16.3/100,000 LB and 185.1/100,000 LB at the level II and level III hospitals. There was a shift from direct to indirect causes of maternal mortality.Conclusions: This safe motherhood referral system was a good strategy in reducing perinatal mortality and direct causes of maternal mortality and decreasing the overall rate of C-sections.

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%.

RESÍDUOS DE FENITROTION em FRUTOS E FOLHAS DETOMATEIRO (Lycopersicon esculentum Mill) ESTAQUEADO

Estudou-se o comportamento dos resíduos de fenitrotion em frutos e folhas de tomateiro estaqueado, através de cromatografia gasosa. O experimento de campo foi instalado quando as plantas tinham 90 dias após o transplante das mudas, e constou de quatro tratamentos: (1) uma aplicação de fenitrotion em dosagem simples, de 100 g i.a./100 litros de água, (2) uma aplicação em dosagem dobrada, de 200 g i.a./100 litros de água, (3) quatro aplicações espaçadas de sete dias, na dosagem simples e (4) testemunha. As amostras de fruto e folha foram colhidas um dia antes da aplicação (-1) e aos zero , 1, 2, 3, 5, 7 e 14 dias após. Basicamente, a metododogia para análises dos resíduos dos frutos e das folhas constou da extração com acetona e partição em clorofórmio; limpeza dos extratos em coluna de florisil (no caso de folhas) e eluição procedida com benzeno. As determinações quantitativas foram feitas por cromatografia gasosa, usando-se detector fotométrico de chama com filtro específico para fósforo. Os resíduos nas folhas foram sempre maiores do que os dos frutos (cerca de 80 vezes, em média) durante todo o período de colheita das amostras. Os valores de meia-vida de degradação de fenitrotion em frutos e folhas foram: 1,6 a 1,9 e 0,7 a 0,8 dia, respectivamente, mostrando uma diminuição mais rápida dos resíduos em folhas. As meias-vidas de persistência foram semelhantes para os dois substratos: 4,2 a 7,3 e 5,6 a 6,2 dias, respectivamente. Os resíduos encontrados nos frutos logo após a aplicação, foram menores que a tolerância oficial (0,5 ppm) para os tratamentos que utilizaram 100 g i.a./100 litros em uma ou quatro pulverizações espaçadas de sete dias. Uma única aplicação de 200 g i.a./100 litros resultou em resíduos menores que 0,5 ppm, desde um dia após a aplicação.

Sono e atividade física habitual em escolares

Pós-graduação em Ciências da Motricidade - IBRC

Validação e cálculo da incerteza de métodos clássicos: titulométricos e espectrofotométricos utilizados na determinação de ácido ascórbico

Pós-graduação em Alimentos e Nutrição - FCFAR

Modelagem em análise de sobrevivencia com eventos recorrentes aplicada a dados da área médica

Pós-graduação em Biometria - IBB

HYGROSCOPIC PROPERTIES OF ORANGE PULP AND PEEL

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

Modelagem e otimização por metodologia de superfícies de resposta: um estudo em arames de aço SAE 9254 para molas automobilísticas

Pós-graduação em Engenharia Mecânica - FEG

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.

A Poisson mixed model with nonnormal random effect distribution

In this paper, we propose a random intercept Poisson model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution. This random effect accommodates (or captures) the overdispersion in the counts and induces within-cluster correlation. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are, in general, required for deriving the marginal models, we obtain the multivariate negative binomial model from a particular parameter setting of the hierarchical model. An iterative process is derived for obtaining the maximum likelihood estimates for the parameters in the multivariate negative binomial model. Residual analysis is proposed and two applications with real data are given for illustration. (C) 2011 Elsevier B.V. All rights reserved.

EOT11a - Global Empirical Ocean Tide model from multi-mission satellite altimetry, with links to model results

EOT11a is a global (E)mpirical (O)cean (T)ide model derived in 2011 by residual analysis of multi-mission satellite (a)ltimeter data. EOT11a includes amplitudes and phases of the main astronomical tides M2, S2, N2, K2, 2N2, O1, K1, P2, and Q1, the non-linear constituent M4, the long period tides Mm and Mf, and the radiational tide S1. Ocean tides as well as loading tides are provided. EOT11a was computed by means of residual tidal analysis of multi-mission altimeter data from TOPEX/Poseidon, ERS-2, ENVISAT, and Jason-1/2, as far as acquired between September 1992 and April 2010. The resolution of 7.5'x7.5' is identical with FES2004 which was used as reference model for the residual tide analysis. The development of EOT11a was funded by the Deutsche Forschungsgemeinschaft (DFG) under grant BO1228/6-2.

Mechanical properties of self-consolidating concrete using conventional concrete models

The objective of this study is to analyze the applicability of current models used for estimating the mechanical properties of conventional concrete to self-consolidating concrete (SCC). The mechanical properties evaluated are modulus of elasticity, tensile strength,and modulus of rupture. As part of the study, it was necessary to build an extensive database that included the proportions and mechanical properties of 627 mixtures from 138 different references. The same models that are currently used for calculating the mechanical properties of conventional concrete were applied to SCC to evaluate their applicability to this type of concrete. The models considered are the ACI 318, ACI 363R, and EC2. These are the most commonly used models worldwide. In the first part of the study, the overall behavior and adaptability of the different models to SCC is evaluated. The specific characterization parameters for each concrete mixture are used to calculate the various mechanical properties applying the different estimation models. The second part of the analysis consists of comparing the experimental results of all the mixtures included in the database with the estimated results to evaluate the applicability of these models to SCC. Various statistical procedures, such as regression analysis and residual analysis, are used to compare the predicted and measured properties. It terms of general applicability, the evaluated models are suitable for estimating the modulus of elasticity, tensile strength, and modulus of rupture of SCC. These models have a rather low sensitivity, however, and adjust well only to mean values. This is because the models use the compressive strength as the main variable to characterize the concrete and do not consider other variables that affect these properties.