Modelo autologístico com aplicação para a doença "morte súbita" dos citrus

The citrus sudden death (CSD) disease affects dramatically citrus trees causing a progressive plant decline and death. The disease has been identified in the late 90's in the main citrus production area of Brazil and since then there are efforts to understand the etiology as well as the mechanisms its spreading. One relevant aspect of such studies is to investigate spatial patterns of the occurrence within a field. Methods for determining whether the spatial pattern is aggregated or not has been frequently used. However it is possible to further explore and describe the data by means of adopting an explicit model to discriminate and quantify effects by attaching parameters to covariates which represent aspects of interest to be investigated. One alternative involves autologistic models, which extend a usual logistic model in order to accommodate spatial effects. In order to implement such model it is necessary to take into account the reuse of data to built spatial covariates, which requires extensions in methodology and algorithms to assess the variance of the estimates. This work presents an application of the autologistic model to data collected at 11 time points from citrus fields affected by CSD. It is shown how the autologistic model is suitable to investigate diseases of this type, as well as a description of the model and the computational aspects necessary for model fitting.A morte súbita dos citros (MSC) é uma doença com efeitos dramáticos em árvores de citros causando declínio progressivo e morte. Ela foi identificada no final da década de 90 em uma das principais áreas de produção no Brasil e desde então esforços são empregados para entender a sua etiologia e os seus mecanismos de dispersão. Um aspecto relevante para estudos é a investigação do padrão espacial da incidência dentro de um campo. Métodos para determinar se o padrão espacial é agregado ou não têm sido freqüentemente utilizados. Entretanto é possível explorar e descrever os dados adotando um modelo explícito, com o qual é possível discriminar e quantificar os efeitos com parâmetros para covariáveis que representam aspectos de interesse investigados. Uma das alternativas é adoção de modelos autologísticos, que estendem o modelo de regressão logística para acomodar efeitos espaciais. Para implementar esse modelo é necessário que se reutilize os dados para extrair covariáveis espaciais, o que requer extensões na metodologia e algoritmos para avaliar a variância das estimativas. Este trabalho apresenta uma aplicação do modelo autologístico a dados coletados em 11 pontos no tempo em um campo de citros afetado pela MSC. É mostrado como o modelo autologístico é apropriado para investigar doenças desse tipo, bem como é feita uma descrição do modelo e dos aspectos computacionais necessários para a estimação dos parâmetros

Bayesian survival analysis with INLA

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS" (Alvares et al., 2021). In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach

Associação entre qualificação profissional e eventos adversos em unidades de tratamento intensivo neonatal e pediátrico

Objetivo: Verificar a associação entre a qualificação dos profissionais de enfermagem e a ocorrência de eventos adversos em unidades de terapia intensiva neonatal e pediátrica.Método: Estudo transversal conduzido em seis unidades de cinco hospitais públicos do Estado do Paraná, Brasil. A coleta de dados ocorreu de abril/2017 a janeiro/2018, com análise retrospectiva e aplicação dos instrumentos Neonatal Trigger Tool e Paediatric Trigger Tool a 79 prontuários, para detectar eventos adversos, questionário autoaplicável a 143 profissionais e consulta aos documentos e registros hospitalares. Os fatores prognósticos de eventos adversos foram capacitação profissional e existência, ou não, de serviço de educação continuada; a análise foi realizada por regressão logística.Resultados: Detectou-se 30 eventos adversos, com prevalência de infecção (n=12;40%) e lesão de pele (n=9;30%). A educação continuada foi identificada como fator protetor para eventos adversos (p≤0,05).Conclusão: atividade educativa foi associada à prevenção de eventos adversos em unidades de terapia intensiva neonatal e pediátrica.Palavras-chave: Unidades de terapia intensiva neonatal. Unidades de terapia intensiva pediátrica. Credenciamento. Dano ao paciente. Segurança do paciente

Bayesian survival analysis with INLA

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS". In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach

Bayesian survival analysis with INLA.

Publication status: PublishedThis tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach

Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations

International audienceAbstract Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research

Um Ambiente para Monitoramento da Morte Súbita dos Citrus

This article describes the implementation and applications of computational-statistical surveillance system for the Citrus Sudden Death Disease. The data is stored in a spatio-temporal TerraLib database and statistical analysis are performed using functions written as a add-on package for the R language called Rcitrus which implements some specialized statistical methods and also interfaces with other packages such as geoR, geoRglm and splancs. The interaction between the statistical environment and the database is provided by the package aRT.Pages: 223-23

The influence of socioeconomic deprivation, access to healthcare and physical environment on old-age survival in Portugal

Spatial inequalities in old-age survival exist in Portugal and might be associated with factors pertaining to three distinct domains: socioeconomic, physical environmental and healthcare. We evaluated the contribution of these factors on the old-age survival across Portuguese municipalities deriving a surrogate measure of life expectancy, a 10-year survival rate that expresses the proportion of the population aged 75-84 years old who reached 85-94. As covariates we used two internationally comparable multivariate indexes: the European deprivation index and the multiple physical environmental deprivation index. A national index was developed to evaluate the access to healthcare. Smoothed rates and odds ratios (OR) were estimated using Bayesian spatial models. Socioeconomic deprivation was found to be the most relevant factor influencing old-age survival in Portugal [women: least deprived areas OR=1.132(1.064-1.207); men OR=1.044(1.001- 1.094)] and explained a sizable amount of the spatial variance in survival, especially among women. Access to healthcare was associated with old-age survival in the univariable model only; results lost significance after adjustment for socioeconomic circumstances [women: higher access to healthcare OR=1.020(0.973- 1.072); men OR=1.021(0.989-1.060)]. Physical environmental deprivation was unrelated with old-age survival. In conclusion, socioeconomic deprivation was the most important determinant in explaining spatial disparities in old-age survival in Portugal, which indicates that policy makers should direct their efforts to tackle socioeconomic differentials between regions